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The development of precision grass measurement technologies is of vital importance to securing the future sustainability of pasture-based livestock production systems. There is potential to increase grassland production in a sustainable manner by achieving a more precise measurement of pasture quantity and quality. This review presents an overview of the most recent seminal research pertaining to the development of precision grass measurement technologies. One of the main obstacles to precision grass measurement, sward heterogeneity, is discussed along with optimal sampling techniques to address this issue. The limitations of conventional grass measurement techniques are outlined and alternative new terrestrial, proximal, and remote sensing technologies are presented. The possibilities of automating grass measurement and reducing labour costs are hypothesised and the development of holistic online grassland management systems that may facilitate these goals are further outlined.
Darren Murphy; Michael Murphy; Bernadette O’Brien; Michael O’Donovan. A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland. Agriculture 2021, 11, 600 .
AMA StyleDarren Murphy, Michael Murphy, Bernadette O’Brien, Michael O’Donovan. A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland. Agriculture. 2021; 11 (7):600.
Chicago/Turabian StyleDarren Murphy; Michael Murphy; Bernadette O’Brien; Michael O’Donovan. 2021. "A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland." Agriculture 11, no. 7: 600.
The objective of this paper was to quantify the economic and environmental effects of changing a dairy farm’s milking start times. Changing morning and evening milking start times could reduce both electricity costs and farm electricity related CO2 emissions. However, this may also involve altering farmer routines which are based on practical considerations. Hence, these changes need to be quantified both in terms of profit/emissions and in terms of how far these milking start times deviate from normal operations. The method presented in this paper optimized the combination of dairy farm infrastructure setup and morning and evening milking start times, based on a weighting variable (α) which assigned relative importance to labor utilization, farm net profit and farm electricity related CO2 emissions. Multi-objective optimization was utilized to assess trade-offs between labor utilization and net profit, as well as labor utilization and electricity related CO2 emissions. For a case study involving a 195 cow Irish dairy farm, when the relative importance of maximizing farm net profit or minimizing farm electricity related CO2 emissions was high, the least common milking start times (06:00 and 20:00) were selected. When the relative importance of labor utilization was high, the most common milking start times (07:00 and 17:00) were selected. The 195 cow farm saved €137 per annum when milking start times were changed from the most common to the least common. Reductions in electricity related CO2 emissions were also seen when the milking start times were changed from most common to least common. However, this reduction in emissions was primarily due to the addition of efficient and renewable technology to the farm. It was deduced that the monetary and environmental benefits of altering farmer milking routines were unlikely to change normal farm operating procedures.
Michael Breen; Michael Murphy; John Upton. Assessing the Effect of Modifying Milking Routines on Dairy Farm Economic and Environmental Performance. AgriEngineering 2021, 3, 266 -277.
AMA StyleMichael Breen, Michael Murphy, John Upton. Assessing the Effect of Modifying Milking Routines on Dairy Farm Economic and Environmental Performance. AgriEngineering. 2021; 3 (2):266-277.
Chicago/Turabian StyleMichael Breen; Michael Murphy; John Upton. 2021. "Assessing the Effect of Modifying Milking Routines on Dairy Farm Economic and Environmental Performance." AgriEngineering 3, no. 2: 266-277.
Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission factors (MEFs), which compared to grid mix emission factors (XEFs), are cumbersome to calculate due to the complex characteristics of national electricity markets. This study, therefore, proposes two merit order-based methods to approximate hourly MEFs and applies them to readily available datasets from 20 European countries for the years 2017–2019. Based on the calculated electricity prices, standardized daily load shifts were simulated which indicated that carbon emissions increased for 8 of the 20 countries and by 2.1% on average. Thus, under specific circumstances, PBDR leads to carbon emissions increases, mainly due to the economic advantage fuel sources such as lignite and coal have in the merit order. MEF-based load shifts reduced the mean resulting carbon emissions by 35%, albeit with 56% lower monetary cost savings compared to price-based load shifts. Finally, by repeating the load shift simulations for different carbon price levels, the impact of the carbon price on the resulting carbon emissions was analyzed. The Spearman correlation coefficient between carbon intensity and marginal cost along the German merit order substantially increased with increasing carbon price. The coefficients were -0.13 for the 2019 carbon price of 24.9 €/t, 0 for 42.6 €/t, and 0.4 for 100.0 €/t. Therefore, with adequate carbon prices, PBDR can be an effective tool for both economical and environmental improvement.
Markus Fleschutz; Markus Bohlayer; Marco Braun; Gregor Henze; Michael D. Murphy. The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices. Applied Energy 2021, 295, 117040 .
AMA StyleMarkus Fleschutz, Markus Bohlayer, Marco Braun, Gregor Henze, Michael D. Murphy. The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices. Applied Energy. 2021; 295 ():117040.
Chicago/Turabian StyleMarkus Fleschutz; Markus Bohlayer; Marco Braun; Gregor Henze; Michael D. Murphy. 2021. "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices." Applied Energy 295, no. : 117040.
In this study, a grey box (GB) model for simulating internal air temperatures in a naturally ventilated nearly zero energy building (nZEB) was developed and calibrated, using multiple data configurations for model parameter selection and an automatic calibration algorithm. The GB model was compared to a white box (WB) model for the same application using identical calibration and validation datasets. Calibrating the GB model using only one week of data produced very accurate results for the calibration periods but led to inconsistent and typically inaccurate results for the validation periods (root mean squared error (RMSE) in validation periods was 229% larger than the RMSE in calibration periods). Using three weeks of data from varying seasons for calibration reduced the model accuracy in the calibration period but substantially increased the model accuracy and generalisation abilities for the validation period, reducing the mean RMSE by over 160%. The use of one week of data increased the standard deviation in parameter selections by over 40% when compared with the three-week calibration datasets. Utilising data from multiple seasons for calibration purposes was found to substantially improve generalisation abilities. When compared to the WB model, the GB model produced slightly less accurate results (mean RMSE of the GB model was 1.5% higher). However, the authors found that employing GB modelling with an automatic model calibration technique reduced the human labour input for simulating internal air temperature of a naturally ventilated nZEB by approximately 90%, relative to WB modelling using a manually calibrated approach.
Michael Murphy; Paul O’Sullivan; Guilherme Carrilho da Graça; Adam O’Donovan. Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods. Energies 2021, 14, 871 .
AMA StyleMichael Murphy, Paul O’Sullivan, Guilherme Carrilho da Graça, Adam O’Donovan. Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods. Energies. 2021; 14 (4):871.
Chicago/Turabian StyleMichael Murphy; Paul O’Sullivan; Guilherme Carrilho da Graça; Adam O’Donovan. 2021. "Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods." Energies 14, no. 4: 871.
The production of milk must be balanced with the sustainable consumption of water resources to ensure the future sustainability of the global dairy industry. Thus, this review article aimed to collate and summarize the literature in the dairy water-usage domain. While green water use (e.g., rainfall) was found to be largest category of water use on both stall and pasture-based dairy farms, on-farm blue water (i.e., freshwater) may be much more susceptible to local water shortages due to the nature of its localized supply through rivers, lakes, or groundwater aquifers. Research related to freshwater use on dairy farms has focused on monitoring, modeling, and analyzing the parlor water use and free water intake of dairy cows. Parlor water use depends upon factors related to milk precooling, farm size, milking systems, farming systems, and washing practices. Dry matter intake is a prominent variable in explaining free water intake variability; however, due to the unavailability of accurate data, some studies have reported moving away from dry matter intake at the expense of prediction accuracy. Machine-learning algorithms have been shown to improve dairy water-prediction accuracy by 23%, which may allow for coarse model inputs without reducing accuracy. Accurate models of on-farm water use allow for an increased number of dairy farms to be used in water footprinting studies, as the need for physical metering equipment is mitigated.
Philip Shine; Michael Murphy; John Upton. A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming. Sustainability 2020, 12, 7201 .
AMA StylePhilip Shine, Michael Murphy, John Upton. A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming. Sustainability. 2020; 12 (17):7201.
Chicago/Turabian StylePhilip Shine; Michael Murphy; John Upton. 2020. "A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming." Sustainability 12, no. 17: 7201.
The aim of this study was to develop a financial and renewable multi-objective optimization (FARMOO) method for dairy farms. Due to increased global milk production and European Union policies concerning renewable energy contributions, the optimization of dairy farms from financial and renewable standpoints is crucial. The FARMOO method found the optimal combination of dairy farm equipment and management practices, based on a trade-off parameter which quantified the relative importance of maximizing farm net profit and maximizing farm renewable contribution. A PV system model was developed and validated to assess the financial performance and renewable contribution of this technology in a dairy farming context. Seven PV system sizes were investigated, ranging from 2 kWp to 11 kWp. Multi-objective optimization using a Genetic Algorithm was implemented to find the optimal combination of equipment and management practices based on the aforementioned trade-off parameter. For a test case of a 195 cow spring calving dairy farm in Ireland, it was found that when the relative importance of farm net profit was high, a PV system was not included in the optimal farm configuration. When net profit and renewable contribution were of equal importance, the optimal farm configuration included an 11 kWp PV system with a scheduled water heating load at 10:00. Multi-objective optimization was carried out for the same test case with the goals of maximizing farm net profit and minimizing farm CO2 emissions. Under this scenario, the optimal farm configuration included an 11 kWp PV system when the relative importance of farm net profit was low. This study included a sensitivity analysis which investigated the use of a 40% grant aid on PV system capital costs. This sensitivity analysis did not significantly improve the financial feasibility of PV systems on dairy farms. Moreover, it was found that load shifting of a farm’s water heating enabled the majority of the PV system’s electricity output to be consumed. Hence the use of batteries with small PV systems on dairy farms may not be necessary. The method described in this study will be used to inform policy and provide decision support relating to PV systems on dairy farms.
M. Breen; J. Upton; M.D. Murphy. Photovoltaic systems on dairy farms: Financial and renewable multi-objective optimization (FARMOO) analysis. Applied Energy 2020, 278, 115534 .
AMA StyleM. Breen, J. Upton, M.D. Murphy. Photovoltaic systems on dairy farms: Financial and renewable multi-objective optimization (FARMOO) analysis. Applied Energy. 2020; 278 ():115534.
Chicago/Turabian StyleM. Breen; J. Upton; M.D. Murphy. 2020. "Photovoltaic systems on dairy farms: Financial and renewable multi-objective optimization (FARMOO) analysis." Applied Energy 278, no. : 115534.
The global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practices.
Philip Shine; John Upton; Paria Sefeedpari; Michael D. Murphy. Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies 2020, 13, 1288 .
AMA StylePhilip Shine, John Upton, Paria Sefeedpari, Michael D. Murphy. Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies. 2020; 13 (5):1288.
Chicago/Turabian StylePhilip Shine; John Upton; Paria Sefeedpari; Michael D. Murphy. 2020. "Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses." Energies 13, no. 5: 1288.
The aim of this paper was to investigate the trade-offs that can be achieved between optimizing the electricity costs of a building integrated microgrid, while simultaneously facilitating high levels of wind penetration in a smart grid. This study applied multi-objective optimization to obtain a daily charge and discharge schedule of a battery bank, which was used to both store electricity from the microgrid and smart grid and could also provide electricity to the building and the smart grid. Multi-objective optimization was employed due to the independent objectives of minimizing building operating cost and maximizing the facilitation of wind energy from the smart grid. The trade-offs between the two objectives were simulated, evaluated and analyzed. A priority weighting factor (α) was applied to each objective. The purpose of α was to vary the importance of each objective relative to the other in an inversely proportional manner. This enabled the algorithm to optimize the battery operating schedule for the economic performance of the microgrid, the facilitation of wind generation on the smart grid or for trade-offs in between. The results present a comprehensive evaluation of 96 scenarios with varying daily weather conditions, building electricity demand, electricity pricing, microgrid output and wind penetration from the smart grid. A multi-objective optimization approach was then applied for each of the 96 scenarios with 11 α values to determine optimal trade-offs in these scenarios. Generally for the 96 scenarios analyzed, when the α value was 20% or higher, the amount of extra wind generation facilitation obtained was negligible while microgrid operating costs continued to increase. The results showed that when changing from an α value of 0% to an α value of 20%, there was a large increase in wind generation facilitation compared to the corresponding increase in cost, with wind generation facilitation increasing from its minimum value to within 89% of its maximum value (10.7% to 14.3% of facilitated wind generation). The corresponding building cost increased from its minimum value to within 13% of its maximum value (€1.14/day to €1.37/day). This produced a cost of approximately €0.06 for every 1% increase in wind generation facilitation. In comparison to this, changing from an α value of 20% to an α value of 100% implied a cost of approximately €3.64 for every 1% increase in wind generation facilitation. These results indicated that smart grids with large percentages of wind penetration may be substantially aided by utilizing the storage capacity of building integrated microgrids for a relatively low monetary cost.
Quang An Phan; Ted Scully; Michael Breen; Michael D. Murphy. Facilitating high levels of wind penetration in a smart grid through the optimal utilization of battery storage in microgrids: An analysis of the trade-offs between economic performance and wind generation facilitation. Energy Conversion and Management 2020, 206, 112354 .
AMA StyleQuang An Phan, Ted Scully, Michael Breen, Michael D. Murphy. Facilitating high levels of wind penetration in a smart grid through the optimal utilization of battery storage in microgrids: An analysis of the trade-offs between economic performance and wind generation facilitation. Energy Conversion and Management. 2020; 206 ():112354.
Chicago/Turabian StyleQuang An Phan; Ted Scully; Michael Breen; Michael D. Murphy. 2020. "Facilitating high levels of wind penetration in a smart grid through the optimal utilization of battery storage in microgrids: An analysis of the trade-offs between economic performance and wind generation facilitation." Energy Conversion and Management 206, no. : 112354.
As the cooling energy demand in buildings is set to increase dramatically in the future, the exploitation of passive solutions like natural ventilation could prove vital in reducing the reliance on mechanical systems. Models that can predict air temperature accurately in naturally ventilated mode are key to understanding the potential of natural ventilation now and in the future. This article presents a simulation based case study of a retrofitted nearly zero energy test-bed university building, in naturally ventilated mode only. The study had three aims: (1) calibration and validation of a whole building energy model, (2) a comparative analysis of occupancy schedules and opening control strategies, and (3) a comparison of researcher and practitioner approaches. Results showed the detailed building model was capable of predicting room level air temperature with a low level of error (0.27 °C ≤ RMSE ≤ 1.50 °C) that was well within the limits of existing calibration standards (MBE ±10%, CVRMSE <20%). The comparative analysis highlighted the need to consider occupancy schedules that have a wide range of diversity, and opening control strategies that reflect the manual and automated relationship in natural ventilation systems. The approach comparison highlighted that both practitioner and researcher approaches to simulating both occupancy schedules and opening control strategies showed similar levels of performance for the application considered. The paper also provides recommendations for those modelling air temperatures and thermal comfort in nearly zero energy buildings.
Adam O' Donovan; Paul D. O' Sullivan; Michael D. Murphy. Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches. Applied Energy 2019, 250, 991 -1010.
AMA StyleAdam O' Donovan, Paul D. O' Sullivan, Michael D. Murphy. Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches. Applied Energy. 2019; 250 ():991-1010.
Chicago/Turabian StyleAdam O' Donovan; Paul D. O' Sullivan; Michael D. Murphy. 2019. "Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches." Applied Energy 250, no. : 991-1010.
This study utilised a previously developed support vector machine (SVM) (trained using empirical data from 56 dairy farms) for predicting and analysing annual dairy farm electricity consumption to help improve the sustainability of the projected expansion of milk production in Ireland. Firstly, the capability of the SVM to predict annual electricity consumption was investigated at both a farm and catchment-level (combined consumption). Electricity consumption data were attained from 16 pasture-based, Irish dairy farms between June 2016 and May 2017 in conjunction with farm data related to herd size, milk production, infrastructural equipment and managerial tendencies, required to generate predictions using the SVM. The SVM predicted annual electricity consumption of dairy farms to within 10.4% (relative prediction error). Concurrently, catchment-level electricity consumption was predicted with an error value less than 5.0%. Secondly, an investigation was carried out to assess the impact of increasing herd size and milk production on dairy farm related electricity consumption at a catchment-level across ten hypothetical infrastructural scenarios. The dairy expansion analysis showed electricity economies of scale across all ten infrastructural scenarios. The greatest reduction in electricity consumption per litre was observed when all farms employed ground water for pre-cooling milk with two additional parlour units, reducing by 4% in 2018, relative to a base scenario (no change to infrastructural equipment). The results presented in this article demonstrate the potential effectiveness of the SVM as a macro-level simulation forecast tool for dairy farm electricity consumption that may be used to quantify the impact of milk production on electricity resources, or to offer decision support to dairy farmers.
Philip Shine; T. Scully; J. Upton; M.D. Murphy. Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine. Applied Energy 2019, 250, 1110 -1119.
AMA StylePhilip Shine, T. Scully, J. Upton, M.D. Murphy. Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine. Applied Energy. 2019; 250 ():1110-1119.
Chicago/Turabian StylePhilip Shine; T. Scully; J. Upton; M.D. Murphy. 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine." Applied Energy 250, no. : 1110-1119.
The objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models. The two models chosen were the nonlinear auto-regressive model with exogenous input (NARX) and the multiple linear regression (MLR) model. The accuracy of these models were assessed using seven different combinations of precipitation, sunshine hours and soil temperature as additional model training inputs. Lactation data (daily milk yield and days in milk) from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database. The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short (10-day), medium (30-day) and long-term (305-day) forecast horizons. The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield (kg), with R2 values greater than 0.7 for 95.5% and 14.7% of total predictions, respectively. The results showed that the introduction of sunshine hours, precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short, medium and long-term forecast horizons. Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60% and 70% of all predictions (for all 39 test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy.
Fan Zhang; John Upton; Laurence Shalloo; Philip Shine; Michael D. Murphy. Effect of introducing weather parameters on the accuracy of milk production forecast models. Information Processing in Agriculture 2019, 7, 120 -138.
AMA StyleFan Zhang, John Upton, Laurence Shalloo, Philip Shine, Michael D. Murphy. Effect of introducing weather parameters on the accuracy of milk production forecast models. Information Processing in Agriculture. 2019; 7 (1):120-138.
Chicago/Turabian StyleFan Zhang; John Upton; Laurence Shalloo; Philip Shine; Michael D. Murphy. 2019. "Effect of introducing weather parameters on the accuracy of milk production forecast models." Information Processing in Agriculture 7, no. 1: 120-138.
The aim of this paper was to develop a Dairy Multi-Objective Optimization (DAIRYMOO) method to carry out multi-objective optimization of dairy farm equipment, management practices and electricity tariffs, optimizing based on a user specified economic and environmental weighting factor. Models of both solar thermal water heating and heat recovery systems were developed, validated, and used as part of a test case for DAIRYMOO. Optimizing dairy farm equipment, management practices and electricity tariffs both economically and environmentally is necessary because of the competing goals of increasing farm milk production and the necessity to reduce agricultural related greenhouse gas emissions. Models of solar thermal water heating and heat recovery systems were created using experimental data to evaluate the economic and environmental performance of these technologies. Multi-objective optimization was used to obtain the optimal selection of equipment, management practices and electricity tariffs which impact electricity related costs and CO2 emissions, based on a user specified economic and environmental weighting factor. A Genetic Algorithm was employed to maximize a combined objective function based on this weighting factor. For a test case with a 195 cow farm, over a ten year time horizon the optimal equipment, management and electricity tariff combination was found using 11 different weighting factor values ranging from 0 to 1. The combined objective function was gradually weighted towards the environmental criterion and away from the economic criterion. It was found that the optimal equipment combination changed incrementally relative to the change in the weighting factor. Furthermore, an analysis was carried out which performed the same multi-objective optimization on the same 195 cow farm, but with a mandatory real time pricing tariff in place. The optimal configurations using the mandatory real time pricing tariff showed that gas water heating was selected regardless of the weighting factor employed. This differed from the results with no mandatory tariff in which a day/night tariff was optimal for all weighting factor values, and electric water heating was optimal for weighting factor values of 0.9 or higher i.e. when the combined objective function was weighted heavily towards the economic criterion. For both analyses, heat recovery systems were not included in the optimal farm configuration unless the weighting factor was 0.2 or less, indicating that the economic performance of these systems was poor. Solar thermal water heating systems were not included in the optimal farm configuration regardless of the weighting factor value. The DAIRYMOO method described in this study will provide useful advice to farmers and policy makers relating to economic and environmental optimization around equipment, management and electricity tariff choices on dairy farms.
M. Breen; Michael D. Murphy; J. Upton. Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms. Applied Energy 2019, 242, 1697 -1711.
AMA StyleM. Breen, Michael D. Murphy, J. Upton. Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms. Applied Energy. 2019; 242 ():1697-1711.
Chicago/Turabian StyleM. Breen; Michael D. Murphy; J. Upton. 2019. "Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms." Applied Energy 242, no. : 1697-1711.
The objectives of this study were to compare the prediction accuracy of two milk prediction models at the individual cow level and to develop, compare and evaluate six input data preprocessing treatments designed to factor parity information into the milk prediction model configuration process. The two models were a nonlinear auto-regressive model with exogenous input and a polynomial curve fitting model. These were tested using six different parity data input treatments. Different combinations of static parity weight, dynamic parity weight and removal of the first lactation data were selected as input treatments. Lactation data from 39 individual cows were extracted from a sample herd of pasture-based Holstein-Friesian cattle located in the south of Ireland and situated in close proximity. The models were trained using three years of historical milk production data and were employed for the prediction of the total daily milk yield of the fourth lactation for each individual cow using a 305-day forecast horizon. The nonlinear auto-regressive model with exogenous input was found to provide higher prediction accuracy than the polynomial curve fitting model for individual cows using each input treatment. An improvement in forecast accuracy was observed in 62% of test cows (24 of 39). However, on average across the entire population, only part of the treatments delivered an increase in accuracy and the success rate varied between test groups. Prediction performance was strongly influenced by the cows' historical milk production relative to parity and also the prediction year. These results highlighted the importance of examining the accuracy of milk prediction models and model training strategies across multiple time horizons. Removal of the first lactation and applying static parity weight were shown to be the two most successful input treatments. The results showed that historical parity weighting trends had a substantial effect on the success rate of the treatments for both milk production forecast models.
F. Zhang; J. Upton; L. Shalloo; M.D. Murphy. Effect of parity weighting on milk production forecast models. Computers and Electronics in Agriculture 2019, 157, 589 -603.
AMA StyleF. Zhang, J. Upton, L. Shalloo, M.D. Murphy. Effect of parity weighting on milk production forecast models. Computers and Electronics in Agriculture. 2019; 157 ():589-603.
Chicago/Turabian StyleF. Zhang; J. Upton; L. Shalloo; M.D. Murphy. 2019. "Effect of parity weighting on milk production forecast models." Computers and Electronics in Agriculture 157, no. : 589-603.
Darren J Murphy; Bernadette O’ Brien; Mohammad Sadegh Askari; Timothy McCarthy; Aidan Magee; Rebekah Burke; Michael D. Murphy. GrassQ - A holistic precision grass measurement and analysis system to optimize pasture based livestock production. 2019 Boston, Massachusetts July 7- July 10, 2019 2019, 1 .
AMA StyleDarren J Murphy, Bernadette O’ Brien, Mohammad Sadegh Askari, Timothy McCarthy, Aidan Magee, Rebekah Burke, Michael D. Murphy. GrassQ - A holistic precision grass measurement and analysis system to optimize pasture based livestock production. 2019 Boston, Massachusetts July 7- July 10, 2019. 2019; ():1.
Chicago/Turabian StyleDarren J Murphy; Bernadette O’ Brien; Mohammad Sadegh Askari; Timothy McCarthy; Aidan Magee; Rebekah Burke; Michael D. Murphy. 2019. "GrassQ - A holistic precision grass measurement and analysis system to optimize pasture based livestock production." 2019 Boston, Massachusetts July 7- July 10, 2019 , no. : 1.
Philip Shine; Michael Breen; John Upton; Adam O’Donovan; Michael D. Murphy. A decision support and optimization platform for energy technology investments on dairy farms. 2019 Boston, Massachusetts July 7- July 10, 2019 2019, 1 .
AMA StylePhilip Shine, Michael Breen, John Upton, Adam O’Donovan, Michael D. Murphy. A decision support and optimization platform for energy technology investments on dairy farms. 2019 Boston, Massachusetts July 7- July 10, 2019. 2019; ():1.
Chicago/Turabian StylePhilip Shine; Michael Breen; John Upton; Adam O’Donovan; Michael D. Murphy. 2019. "A decision support and optimization platform for energy technology investments on dairy farms." 2019 Boston, Massachusetts July 7- July 10, 2019 , no. : 1.
The objective of this paper was to develop a discrete infrastructure optimization model for economic assessment on dairy farms (DIOMOND) to maximize return on investment (ROI) in dairy farm infrastructure over a specified time horizon. Optimizing ROI in dairy farm infrastructure is essential since the choice of technology, electricity tariff and management practices affects the economic performance of the farm. The abolition of European Union milk quotas has started to increase investments in on-farm equipment, necessitating a means of financial decision support for farmers. The optimization of ROI in dairy farm infrastructure within DIOMOND was accomplished by obtaining the optimal selection of equipment, management processes and electricity tariff which affect dairy farm electricity and capital costs and consequently ROI for new infrastructure. Optimization algorithms were applied to previously published dairy farm electricity consumption and ROI models to obtain the combination of decision variables pertaining to dairy farm technology, management practices and electricity tariff which yielded the maximum ROI for a dairy farm over a specified time horizon, compared with a base investment scenario. Furthermore, an assessment was carried out to find the most suitable optimization algorithm for use in maximizing ROI in dairy farm infrastructure. Five optimization algorithms, namely Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Tabu Search and Dynamic Programming, were assessed and their optimization performance compared. The Genetic Algorithm was selected due to its superior efficiency and performance. For a test case involving a 195 cow farm the optimal combination of farm technology, management and electricity tariff was found, resulting in a level of improvement in ROI of 26.31% over a ten year time horizon compared to a base investment scenario. The applicability of DIOMOND to a real world scenario was demonstrated successfully. Therefore it is anticipated that DIOMOND will provide a comprehensive tool to deliver substantial monetary savings and assist in decision making for dairy farmers, including those investing in new technology.
M. Breen; J. Upton; M.D. Murphy. Development of a discrete infrastructure optimization model for economic assessment on dairy farms (DIOMOND). Computers and Electronics in Agriculture 2018, 156, 508 -522.
AMA StyleM. Breen, J. Upton, M.D. Murphy. Development of a discrete infrastructure optimization model for economic assessment on dairy farms (DIOMOND). Computers and Electronics in Agriculture. 2018; 156 ():508-522.
Chicago/Turabian StyleM. Breen; J. Upton; M.D. Murphy. 2018. "Development of a discrete infrastructure optimization model for economic assessment on dairy farms (DIOMOND)." Computers and Electronics in Agriculture 156, no. : 508-522.
This paper proposes strategies to optimize the daily charge and discharge schedule of a battery bank, in order to minimize the operating cost of a building that uses renewable energy sources. The schedule was optimized using a range of battery charge and discharge rates over a 24 h period. These rates were controlled using a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO), which utilized day-ahead prediction data for electricity consumption and electricity price, as well as electricity output from a photovoltaic system and a wind turbine. The results showed that the building operating costs decreased as the number of available charge and discharge rates was increased. The average daily operating cost was reduced by up to 31% using the GA and by up to 28% using the PSO, compared to the scenario where no battery was used. Furthermore, the reduction in average daily operating costs began to plateau as the number of charge and discharge rates reached 12. It was also shown that the scaling of irradiance, wind speed and electricity price inputs impacted the optimized daily operating cost of the building. A sensitivity analysis was conducted to investigate how this scaling of inputs affected the overall performance of the GA. It was found that the optimized daily operating costs were almost unchanged after numerous scaling percentages were applied to the electricity price, with additional cost reductions of up to 3% compared to the scenario where no scaling percentages were applied. In contrast, scaling percentages applied to weather data were found to have a more significant impact on the optimized operating costs, with additional cost reductions of up to 17% compared to the scenario where no scaling percentages were applied. Moreover, a non-linear relationship was observed between the weather data scaling percentage and optimized daily cost.
Quang An Phan; Ted Scully; Michael Breen; Michael D. Murphy. Determination of optimal battery utilization to minimize operating costs for a grid-connected building with renewable energy sources. Energy Conversion and Management 2018, 174, 157 -174.
AMA StyleQuang An Phan, Ted Scully, Michael Breen, Michael D. Murphy. Determination of optimal battery utilization to minimize operating costs for a grid-connected building with renewable energy sources. Energy Conversion and Management. 2018; 174 ():157-174.
Chicago/Turabian StyleQuang An Phan; Ted Scully; Michael Breen; Michael D. Murphy. 2018. "Determination of optimal battery utilization to minimize operating costs for a grid-connected building with renewable energy sources." Energy Conversion and Management 174, no. : 157-174.
This study analysed the performance of a range of machine learning algorithms when applied to the prediction of electricity and on-farm direct water consumption on Irish dairy farms. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, commercial Irish dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed for their predictive power of monthly electricity and water consumption, respectively. These variables were related to milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions. A CART decision tree algorithm, a random forest ensemble algorithm, an artificial neural network and a support vector machine algorithm were used to predict both water and electricity consumption. The methodology employed backward sequential variable selection to exclude variables, which added little predictive power. It also applied hyper-parameter tuning with nested cross-validation for calculating the prediction accuracy for each model on unseen data (data not utilised for model development). Electricity consumption was predicted to within 12% (relative prediction error (RPE)) using a support vector machine, while the random forest predicted water consumption to within 38%. Overall, the developed machine-learning models improved the RPE of electricity and water consumption by 54% and 23%, respectively, when compared to results previously obtained using a multiple linear regression approach. Further analysis found that during the January, February, November and December period, the support vector machine overpredicted electricity consumption by 4% (mean percentage error (MPE)) and water consumption by 21% (MPE), on average. However, overprediction was greatly reduced during the March – October period with overprediction of electricity consumption reduced to 1% while the overprediction of water consumption reduced to 8%. This was attributed to a phase shift between farms, where some farms produce milk all year round, some dry off earlier/later than others and some farms begin milking earlier/later resulting in an increased the coefficient of variance of milk production making it more difficult to model electricity and water accurately. Concurrently, large negative correlations were calculated between the number of dairy cows and absolute prediction error for electricity and water, respectively, suggesting improvements in electricity and water prediction accuracy may be achieved with increasing dairy cow numbers. The developed machine learning models may be utilised to provide key decision support information to both dairy farmers and policy makers or as a tool for conducting macro scale environmental analysis.
Philip Shine; M.D. Murphy; J. Upton; T. Scully. Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture 2018, 150, 74 -87.
AMA StylePhilip Shine, M.D. Murphy, J. Upton, T. Scully. Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture. 2018; 150 ():74-87.
Chicago/Turabian StylePhilip Shine; M.D. Murphy; J. Upton; T. Scully. 2018. "Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms." Computers and Electronics in Agriculture 150, no. : 74-87.
An analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises.
Philip Shine; T. Scully; J. Upton; M.D. Murphy. Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture 2018, 148, 337 -346.
AMA StylePhilip Shine, T. Scully, J. Upton, M.D. Murphy. Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture. 2018; 148 ():337-346.
Chicago/Turabian StylePhilip Shine; T. Scully; J. Upton; M.D. Murphy. 2018. "Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms." Computers and Electronics in Agriculture 148, no. : 337-346.
This study compared multiple linear regression (MLR) and support vector machine (SVM) models for predicting the annual electricity consumption of 20 Irish dairy farms, at a farm and catchment (combined) level. Model input variables were constrained to milk production, stock numbers, infrastructural equipment and managerial procedures to allow predictions to take place on a large scale without the use of specialized equipment. The SVM model has previously been shown to reduce the prediction error of monthly electricity consumption by 54% compared to the MLR model. Results found both the MLR and SVM models predicted annual electricity consumption per farm to within 20%. However, the error of the SVM model reduced to 9% when two farms with the greatest monthly prediction errors were removed. With herd sizes in excess of 190 dairy cows, these two farms were found to represent less than 3.3% of the Irish dairy farm demographic. Regarding the ability of each model to predict catchment level electricity consumption, the MLR model prediction resulted in an error of 4% while the SVM prediction resulted in an error of 9%. The improved accuracy of the MLR model when predicting electricity consumption at a catchment level was respective of a greater balance between the under and over prediction of electricity consumption across the 20 dairy farms. These models may be utilized to provide key decision support information to both dairy farmers and policy makers, or as a tool for conducting macro scale environmental analysis, for marketing Irish dairy products abroad.
Philip Shine; John Upton; Ted Scully; Laurence Shalloo; Michael D. Murphy. Comparing multiple linear regression and support vector machine models for predicting electricity consumption on pasture based dairy farms. 2018 Detroit, Michigan July 29 - August 1, 2018 2018, 1 .
AMA StylePhilip Shine, John Upton, Ted Scully, Laurence Shalloo, Michael D. Murphy. Comparing multiple linear regression and support vector machine models for predicting electricity consumption on pasture based dairy farms. 2018 Detroit, Michigan July 29 - August 1, 2018. 2018; ():1.
Chicago/Turabian StylePhilip Shine; John Upton; Ted Scully; Laurence Shalloo; Michael D. Murphy. 2018. "Comparing multiple linear regression and support vector machine models for predicting electricity consumption on pasture based dairy farms." 2018 Detroit, Michigan July 29 - August 1, 2018 , no. : 1.