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The aim of this paper is to shed light on the question regarding whether the integration of an electric battery as a part of a domestic installation may increase its energy efficiency in comparison with a conventional case. When a battery is included in such an installation, two types of electrical conversion must be considered, i.e., AC/DC and DC/AC, and hence the corresponding losses due to these converters must not be forgotten when performing the analysis. The efficiency of the whole system can be increased if one of the mentioned converters is avoided or simply when its dimensioning is reduced. Possible ways to achieve this goal can be: to use electric vehicles as DC suppliers, the use of as many DC home devices as possible, and LED lighting or charging devices based on renewables. With all this in mind, several scenarios are proposed here in order to have a look at all possibilities concerning AC and DC powering. With the aim of checking these scenarios using real data, a case study is analyzed by operating with electricity consumption mean values.
Daniel Villanueva; Moisés Cordeiro-Costas; Andrés Feijóo-Lorenzo; Antonio Fernández-Otero; Edelmiro Miguez-García. Towards DC Energy Efficient Homes. Applied Sciences 2021, 11, 6005 .
AMA StyleDaniel Villanueva, Moisés Cordeiro-Costas, Andrés Feijóo-Lorenzo, Antonio Fernández-Otero, Edelmiro Miguez-García. Towards DC Energy Efficient Homes. Applied Sciences. 2021; 11 (13):6005.
Chicago/Turabian StyleDaniel Villanueva; Moisés Cordeiro-Costas; Andrés Feijóo-Lorenzo; Antonio Fernández-Otero; Edelmiro Miguez-García. 2021. "Towards DC Energy Efficient Homes." Applied Sciences 11, no. 13: 6005.
Nowadays, there is a growing trend to incorporate renewables in electrical power systems and, in particular, wind energy, which has become an important primary source in the electricity mix of many countries, where wind farms have been proliferating in recent years. This circumstance makes it particularly interesting to understand wind behavior because generated power depends on it. In this paper, a method is proposed to synthetically generate sequences of wind speed values satisfying two important constraints. The first consists of fitting the given statistical distributions, as the generally accepted fact is assumed that the measured wind speed in a location follows a certain distribution. The second consists of imposing spatial and temporal correlations among the simulated wind speed sequences. The method was successfully checked under different scenarios, depending on variables, such as the number of locations, the duration of the data collection period or the size of the simulated series, and the results were of high accuracy.
Moisés Cordeiro-Costas; Daniel Villanueva; Andrés Feijóo-Lorenzo; Javier Martínez-Torres. Simulation of Wind Speeds with Spatio-Temporal Correlation. Applied Sciences 2021, 11, 3355 .
AMA StyleMoisés Cordeiro-Costas, Daniel Villanueva, Andrés Feijóo-Lorenzo, Javier Martínez-Torres. Simulation of Wind Speeds with Spatio-Temporal Correlation. Applied Sciences. 2021; 11 (8):3355.
Chicago/Turabian StyleMoisés Cordeiro-Costas; Daniel Villanueva; Andrés Feijóo-Lorenzo; Javier Martínez-Torres. 2021. "Simulation of Wind Speeds with Spatio-Temporal Correlation." Applied Sciences 11, no. 8: 3355.
For decades of wind energy technology developments, much research on the subject has been carried out, and this has given rise to many works encompassing different topics related to it. As a logical consequence of such a research and editorial activity, state-of-the-art review works have also been published, reporting about a wide variety of research proposals. Review works are particularly interesting documents for researchers because they try to gather different research works on the same topic present their achievements to researchers. They act, in a way, as a guidance for researchers to quickly access the most meaningful works. The proposal of this paper consists of going one step further, and to present a review of state-of-the-art review works on wind-energy-related issues. A classification into several main topics in the field of energy research has been done, and review works that can be classified in all these areas have been searched, analyzed, and commented on throughout the paper.
Manisha Sawant; Sameer Thakare; A. Rao; Andrés Feijóo-Lorenzo; Neeraj Bokde. A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics. Energies 2021, 14, 2041 .
AMA StyleManisha Sawant, Sameer Thakare, A. Rao, Andrés Feijóo-Lorenzo, Neeraj Bokde. A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics. Energies. 2021; 14 (8):2041.
Chicago/Turabian StyleManisha Sawant; Sameer Thakare; A. Rao; Andrés Feijóo-Lorenzo; Neeraj Bokde. 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics." Energies 14, no. 8: 2041.
According to the United Nation’s World Water Development Report, by 2050 more than 50% of the world’s population will be under high water scarcity. To avoid water stress, water resources are needed to be managed more securely. Smart water technology (SWT) has evolved for proper management and saving of water resources. Smart water system (SWS) uses sensor, information, and communication technology (ICT) to provide real-time monitoring of data such as pressure, water ow, water quality, moisture, etc. with the capability to detect any abnormalities such as non-revenue water (NRW) losses, water contamination in the water distribution system (WDS). It makes water and energy utilization more efficient in the water treatment plant and agriculture. In addition, the standardization of data format i.e., use of Water Mark UP language 2.0 has made data exchange easier for between different water authorities. This review research exhibits the current state-of-the-art of the on-going SWT along with present challenges and future scope on the mentioned technologies. A conclusion is drawn that smart technologies can lead to better water resource management, which can lead to the reduction of water scarcity worldwide. High implementation cost may act as a barrier to the implementation of SWT in developing countries, whereas data security and its reliability along with system ability to give accurate results are some of the key challenges in its field implementation.
Aditya Dinesh Gupta; Prerna Pandey; Andrés Feijóo; Zaher Mundher Yaseen; Neeraj Dhanraj Bokde. Smart Water Technology for Efficient Water Resource Management: A Review. Energies 2020, 13, 6268 .
AMA StyleAditya Dinesh Gupta, Prerna Pandey, Andrés Feijóo, Zaher Mundher Yaseen, Neeraj Dhanraj Bokde. Smart Water Technology for Efficient Water Resource Management: A Review. Energies. 2020; 13 (23):6268.
Chicago/Turabian StyleAditya Dinesh Gupta; Prerna Pandey; Andrés Feijóo; Zaher Mundher Yaseen; Neeraj Dhanraj Bokde. 2020. "Smart Water Technology for Efficient Water Resource Management: A Review." Energies 13, no. 23: 6268.
Wind farms (WFs) are important components of smart grid. The modeling and optimal planning of the WF is preliminary before its construction. In this paper, a bi-level multi-objective optimization framework is presented, with the aim of simultaneously designing the configuration of wind turbines (WTs) as well as the topology of electrical collector system in an offshore WF. The installation capacity of the WF, the positioning of the WTs and the planning scheme of the electrical system are balanced to achieve a better performance of the WF. In this proposal, there is an outer layer along with two inner layers. The objectives of the outer-layer model are the maximization of the WF’s daily profit rate, the daily average capacity factor, and power quality. It is tackled by the Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The objectives of the two inner layer models are to determine the topology of the electrical system and the generation schedule of other generators, and are solved by means of the Binary Particle Swarm Optimization (BPSO) algorithm and the quadratic programming (QP) method respectively. The WF is assumed to be connected to the IEEE-24 bus test system. The simulation results validate the adaptability and effectiveness of the proposed approach with the main factors that affect the WF layout being analyzed.
Siyu Tao; Qingshan Xu; Andres Feijoo; Gang Zheng. Joint Optimization of Wind Turbine Micrositing and Cabling in an Offshore Wind Farm. IEEE Transactions on Smart Grid 2020, 12, 834 -844.
AMA StyleSiyu Tao, Qingshan Xu, Andres Feijoo, Gang Zheng. Joint Optimization of Wind Turbine Micrositing and Cabling in an Offshore Wind Farm. IEEE Transactions on Smart Grid. 2020; 12 (1):834-844.
Chicago/Turabian StyleSiyu Tao; Qingshan Xu; Andres Feijoo; Gang Zheng. 2020. "Joint Optimization of Wind Turbine Micrositing and Cabling in an Offshore Wind Farm." IEEE Transactions on Smart Grid 12, no. 1: 834-844.
Nowadays, common electrical household appliances are mostly being powered by means of alternate current (AC), although there are cases where direct current (DC) is used instead. In all cases, internal devices are supplied with DC, and this fact involves there are losses due to the need for AC/DC converters. At the same time, most electrical home consumption takes place during peak hours when electricity is more expensive in many electricity markets. The addition of a battery in these installations permits storing electrical energy during certain periods of the day with the aim of supplying it during other ones—when this operation is more efficient or convenient—simultaneously reducing costs and greenhouse gas emissions. In this paper, a comparison is proposed between three possible home consumption scenarios, i.e., one consisting of a current AC system, one consisting of an AC system with a battery, and a third consisting of a hybrid AC/DC system with a battery.
Daniel Villanueva; Moisés Cordeiro; Andrés Feijóo; Edelmiro Míguez; Antonio Fernández. Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions. Applied Sciences 2020, 10, 5891 .
AMA StyleDaniel Villanueva, Moisés Cordeiro, Andrés Feijóo, Edelmiro Míguez, Antonio Fernández. Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions. Applied Sciences. 2020; 10 (17):5891.
Chicago/Turabian StyleDaniel Villanueva; Moisés Cordeiro; Andrés Feijóo; Edelmiro Míguez; Antonio Fernández. 2020. "Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions." Applied Sciences 10, no. 17: 5891.
This paper presents a review on wind farm (WF) layout optimization with multiple types of wind turbines (WTs). Compared with uniform WF layout optimization, the three key research points of this topic are WT type selection, wake modelling and optimization algorithm design. Firstly, a series of WT power curves are demonstrated with a WT type selection method to choose the most suitable ones. When calculating the WF power output, a one-dimensional (1D) and a two-dimensional (2D) wake models are briefly introduced and a newly-developed three-dimensional (3D) wake model is described in detail for the application in the nonuniform WF layout optimization. The objective functions, constraints and optimization algorithms used in the literature are reviewed and the optimization framework is built. Case studies are carried out on two real-world WFs. One is the Greater Gabbard offshore WF, on a flat area with an irregular shape and the other is Huade II onshore WF, on a mountainous area with a square shape. The aim of this paper is to shed light on the most significant aspects in the nonuniform WF optimization design based on the summary of the latest works. In addition, future works have been pointed out in the conclusion.
Siyu Tao; Qingshan Xu; Andrés Feijóo; Gang Zheng; Jiemin Zhou. Nonuniform wind farm layout optimization: A state-of-the-art review. Energy 2020, 209, 118339 .
AMA StyleSiyu Tao, Qingshan Xu, Andrés Feijóo, Gang Zheng, Jiemin Zhou. Nonuniform wind farm layout optimization: A state-of-the-art review. Energy. 2020; 209 ():118339.
Chicago/Turabian StyleSiyu Tao; Qingshan Xu; Andrés Feijóo; Gang Zheng; Jiemin Zhou. 2020. "Nonuniform wind farm layout optimization: A state-of-the-art review." Energy 209, no. : 118339.
Over the last decades, wind energy has been arising as one of the most promising sources for the future of energy supply, and this trend should be reinforced in the future due to the foreseeable environmental and climatological catastrophe. Therefore, all technologies and issues regarding its development are relevant. Among them, research on wind turbine power curve modeling is of importance for stakeholders and researchers because it allows them to easily obtain information about the amount of power and energy that can be captured from the primary resource, i.e., the wind. The task can be simplified by means of the use of wind turbine power curve models, and many researchers have been presenting their contributions on the topic in parallel with such a development. In this paper, a review on the formulation of wind turbine deterministic power curve models is presented.
Daniel Villanueva; Andrés Feijóo. A Review on Wind Turbine Deterministic Power Curve Models. Applied Sciences 2020, 10, 4186 .
AMA StyleDaniel Villanueva, Andrés Feijóo. A Review on Wind Turbine Deterministic Power Curve Models. Applied Sciences. 2020; 10 (12):4186.
Chicago/Turabian StyleDaniel Villanueva; Andrés Feijóo. 2020. "A Review on Wind Turbine Deterministic Power Curve Models." Applied Sciences 10, no. 12: 4186.
Power curves provided by wind turbine manufacturers are obtained under certain conditions that are different from those of real life operation and, therefore, they actually do not describe the behavior of these machines in wind farms. In those cases where one year of data is available, a logistic function may be fitted and used as an accurate model for such curves, with the advantage that it describes the power curve by means of a very simple mathematical expression. Building such a curve from data can be achieved by different methods, such as using mean values or, alternatively, all the possible values for given intervals. However, when using the mean values, some information is missing and when using all the values the model obtained can be wrong. In this paper, some methods are proposed and applied to real data for comparison purposes. Among them, the one that combines data clustering and simulation is recommended in order to avoid some errors made by the other methods. Besides, a data filtering recommendation and two different assessment procedures for the error provided by the model are proposed.
Daniel Villanueva; Adrián Sixto; Andrés Feijóo; Antonio Fernández; Edelmiro Miguez. Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data. Applied Sciences 2020, 10, 3317 .
AMA StyleDaniel Villanueva, Adrián Sixto, Andrés Feijóo, Antonio Fernández, Edelmiro Miguez. Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data. Applied Sciences. 2020; 10 (9):3317.
Chicago/Turabian StyleDaniel Villanueva; Adrián Sixto; Andrés Feijóo; Antonio Fernández; Edelmiro Miguez. 2020. "Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data." Applied Sciences 10, no. 9: 3317.
In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.
Neeraj Bokde; Andrés Feijóo; Nadhir Al-Ansari; Siyu Tao; Zaher Mundher Yaseen. The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling. Energies 2020, 13, 1666 .
AMA StyleNeeraj Bokde, Andrés Feijóo, Nadhir Al-Ansari, Siyu Tao, Zaher Mundher Yaseen. The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling. Energies. 2020; 13 (7):1666.
Chicago/Turabian StyleNeeraj Bokde; Andrés Feijóo; Nadhir Al-Ansari; Siyu Tao; Zaher Mundher Yaseen. 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling." Energies 13, no. 7: 1666.
The advances in the manufacturing industry make it possible to install wind turbines (WTs) with large capacities in offshore wind farms (OWFs) in deep water areas far away from the coast where there are the best wind resources. This paper proposes a novel method for OWF optimal planning in deep water areas with a circular boundary. A three-dimensional model of the planning area’s seabed is established in a cylindrical coordinate. Two kinds of WTs with capacities of 4 and 8 MW respectively are supposed to be mixed-installed in that area. Baseline cases are analyzed and compared to verify the superiority of a circular layout pattern and the necessity of a non-uniform installation. Based on the establishment of the optimization model and a realistic wind condition, a novel heuristic algorithm, i.e., the whale optimization algorithm (WOA), is applied to solve the problem to obtain the type selection and coordinates of WTs simultaneously. Finally, the feasibility and advantages of the proposed scheme are identified and discussed according to the simulation results.
Siyu Tao; Andrés Feijóo; Jiemin Zhou; Gang Zheng. Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout. Energies 2020, 13, 556 .
AMA StyleSiyu Tao, Andrés Feijóo, Jiemin Zhou, Gang Zheng. Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout. Energies. 2020; 13 (3):556.
Chicago/Turabian StyleSiyu Tao; Andrés Feijóo; Jiemin Zhou; Gang Zheng. 2020. "Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout." Energies 13, no. 3: 556.
The high penetration of wind energy in electrical power systems presents challenges for all operators. For the wind farm (WF) planners, one of these challenges is optimizing its layout with a set of constraints. This paper proposes a bi-hierarchy optimization scheme to determine the capacity and layout of a grid-connected WF. The environmental impacts involved by the installation of a WF have been taken into consideration in the problem. The first-layer model optimizes the WF capacity and configuration with minimized comprehensive generation cost of wind energy and two sets of constraints. The sound pressure level (SPL) limit of the noise emitted by the wind turbines (WTs) is handled to be one of the constraints of the first-layer model. The second-layer model determines the generation schedule of other conventional generators. A Gaussian wake model is applied to calculate the effective wind speed for each WT. For the simulations, the WF is supposed to be integrated in the IEEE 30-bus test system. The wild goats algorithm (WGA) and the quadratic programming (QP) method are used to solve the problem. The simulation results validate the effectiveness of the proposed model and prove that environmental influences of WFs should not be ignored during the planning stage.
Siyu Tao; Qingshan Xu; Andres Feijoo; Peng Hou; Gang Zheng. Bi-Hierarchy Optimization of a Wind Farm Considering Environmental Impact. IEEE Transactions on Sustainable Energy 2020, 11, 2515 -2524.
AMA StyleSiyu Tao, Qingshan Xu, Andres Feijoo, Peng Hou, Gang Zheng. Bi-Hierarchy Optimization of a Wind Farm Considering Environmental Impact. IEEE Transactions on Sustainable Energy. 2020; 11 (4):2515-2524.
Chicago/Turabian StyleSiyu Tao; Qingshan Xu; Andres Feijoo; Peng Hou; Gang Zheng. 2020. "Bi-Hierarchy Optimization of a Wind Farm Considering Environmental Impact." IEEE Transactions on Sustainable Energy 11, no. 4: 2515-2524.
In this paper, an application of the Jaya Algorithm (JA) is presented, to develop an operation optimization model for the Mula reservoir, located on the upper Godavari Basin, in India. The mentioned algorithm is a relatively new optimization technique, which is algorithm-specific and parameterless. In JA, there is no need for algorithm-specific parameter tuning, unlike with other heuristic techniques. To test its applicability, the model performance has been compared with that of other models for hypothetical four reservoir system studies available in the literature. Simulations for hypothetical four reservoir system have proven that JA is a better solution for a number of Function Evaluations when compared with the results obtained by means of other evolutionary methods such as Genetic Algorithms, Particle Swarm Optimization, Elitist Mutated Particle Swarm Optimization, and Weed Optimization Algorithm models reported in previous studies. Simulations have been carried out for real time operation of the Mula reservoir, and have revealed its superior performance when comparing the water releases proposed by it and the ones proposed by existing policy. Hence, from the two case studies presented, it can be concluded that the JA has potential in the field of reservoir operation and can be further explored to operation optimization of existing multi-reservoir system, with lower computations.
Vartika Paliwal; Aniruddha D. Ghare; Ashwini B. Mirajkar; Neeraj Dhanraj Bokde; Andrés Elías Feijóo Lorenzo. Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm. Sustainability 2019, 12, 84 .
AMA StyleVartika Paliwal, Aniruddha D. Ghare, Ashwini B. Mirajkar, Neeraj Dhanraj Bokde, Andrés Elías Feijóo Lorenzo. Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm. Sustainability. 2019; 12 (1):84.
Chicago/Turabian StyleVartika Paliwal; Aniruddha D. Ghare; Ashwini B. Mirajkar; Neeraj Dhanraj Bokde; Andrés Elías Feijóo Lorenzo. 2019. "Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm." Sustainability 12, no. 1: 84.
This work presents a computational method for the simulation of wind speeds and for the calculation of the statistical distributions of wind farm (WF) power curves, where the wake effects and terrain features are taken into consideration. A three-parameter (3-P) logistic function is used to represent the wind turbine (WT) power curve. Wake effects are simulated by means of the Jensen’s wake model. Wind shear effect is used to simulate the influence of the terrain on the WTs located at different altitudes. An analytical method is employed for deriving the probability density function (PDF) of the WF power output, based on the Weibull distribution for describing the cumulative wind speed behavior. The WF power curves for four types of terrain slopes are analyzed. Finally, simulations applying the Monte Carlo method on different sample sizes are provided to validate the proposed model. The simulation results indicate that this approximated formulation is a possible substitute for WF output power estimation, especially for the scenario where WTs are built on a terrain with gradient.
Siyu Tao; Qingshan Xu; Andrés Feijóo; Stefanie Kuenzel; Neeraj Bokde; Tao; Xu. Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient. Energies 2019, 12, 2482 .
AMA StyleSiyu Tao, Qingshan Xu, Andrés Feijóo, Stefanie Kuenzel, Neeraj Bokde, Tao, Xu. Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient. Energies. 2019; 12 (13):2482.
Chicago/Turabian StyleSiyu Tao; Qingshan Xu; Andrés Feijóo; Stefanie Kuenzel; Neeraj Bokde; Tao; Xu. 2019. "Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient." Energies 12, no. 13: 2482.
Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.
Neeraj Bokde; Andrés Feijóo; Daniel Villanueva; Kishore Kulat. A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction. Energies 2019, 12, 254 .
AMA StyleNeeraj Bokde, Andrés Feijóo, Daniel Villanueva, Kishore Kulat. A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction. Energies. 2019; 12 (2):254.
Chicago/Turabian StyleNeeraj Bokde; Andrés Feijóo; Daniel Villanueva; Kishore Kulat. 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction." Energies 12, no. 2: 254.
Wind energy is a variable energy source with a growing presence in many electrical networks across the world. Wind-speed prediction has become an important tool for many agents involved in energy markets. In this paper, an approach to this problem is proposed by means of a novel method that outperforms results obtained by current direct and indirect wind-power prediction procedures. The first difference is that it is not strictly a direct or indirect method in the conventional sense because it uses information from both wind-speed and wind-power data series to obtain a wind-power series. The second difference is that it smooths down the wind-power series obtained in the first stage, and uses the resulting series for predicting new wind-power values. The process of smoothing is based on the label sequence generation process discussed in the pattern sequence forecasting algorithm and the Naive Bayesian method-based matching process. The result is a less chaotic way to predict wind speed than those offered by other existing methods. It has been assessed in multiple simulations, for which three different error measures have been used.
Neeraj Bokde; Andrés Feijóo; Daniel Villanueva; Kishore Kulat. A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods. Energies 2018, 11, 2923 .
AMA StyleNeeraj Bokde, Andrés Feijóo, Daniel Villanueva, Kishore Kulat. A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods. Energies. 2018; 11 (11):2923.
Chicago/Turabian StyleNeeraj Bokde; Andrés Feijóo; Daniel Villanueva; Kishore Kulat. 2018. "A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods." Energies 11, no. 11: 2923.
The representation of a wind turbine power curve by means of the cumulative distribution function of a Weibull distribution is investigated in this paper, after having observed the similarity between such a function and real WT power curves. The behavior of wind speed is generally accepted to be described by means of Weibull distributions, and this fact allows researchers to know the frequency of the different wind speeds. However, the proposal of this work consists of using these functions in a different way. The goal is to use Weibull functions for representing wind speed against wind power, and due to this, it must be clear that the interpretation is quite different. This way, the resulting functions cannot be considered as Weibull distributions, but only as Weibull functions used for the modeling of WT power curves. A comparison with simulations carried out by assuming logistic functions as power curves is presented. The reason for using logistic functions for this validation is that they are very good approximations, while the reasons for proposing the use of Weibull functions are that they are continuous, simpler than logistic functions and offer similar results. Additionally, an explanation about a software package has been discussed, which makes it easy to obtain Weibull functions for fitting WT power curves.
Neeraj Bokde; Andrés Feijóo; Daniel Villanueva. Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function. Applied Sciences 2018, 8, 1757 .
AMA StyleNeeraj Bokde, Andrés Feijóo, Daniel Villanueva. Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function. Applied Sciences. 2018; 8 (10):1757.
Chicago/Turabian StyleNeeraj Bokde; Andrés Feijóo; Daniel Villanueva. 2018. "Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function." Applied Sciences 8, no. 10: 1757.
Preprocessing methods improve prediction accuracy in a significant way. Generally, they stabilize data series mean and variance and remove irregularities. In this paper, two of these methods have been used and compared in the Pattern Sequence based Forecasting (PSF) algorithm. These preprocessing methods are based on differencing and decomposition principles. In the decomposition approach, the Ensemble Empirical Mode Decomposition-Pattern Sequence based Forecasting (EEMD-PSF) model is used. It decomposes the data into finite numbers of subseries and improves the performance of the PSF method. In the Difference Pattern Sequence based Forecasting (DPSF) method, the effects of trends, seasonality, and irregularity components are reduced to some extent and their consequences are tested on distinct datasets with different patterns. While comparing the effects of these preprocessing methods with the effect of the PSF method for sets of wind speed data collected in the autonomous region of Galicia, Spain, and National Renewable Energy Laboratory (NREL), USA, in terms of prediction accuracy, both methods have performed better than the contemporary methods including single PSF, ARIMA, and LSSVM methods. In terms of computational time comsumption, the DPSF method has outperformed the results of the EEMD-PSF model. The simulations revealed that the hybridization of preprocessing and PSF methods has significantly outperformed other state-of-the-art methods for short term wind speed prediction.
Neeraj Bokde; Andrés Feijóo; Kishore Kulat. Analysis of differencing and decomposition preprocessing methods for wind speed prediction. Applied Soft Computing 2018, 71, 926 -938.
AMA StyleNeeraj Bokde, Andrés Feijóo, Kishore Kulat. Analysis of differencing and decomposition preprocessing methods for wind speed prediction. Applied Soft Computing. 2018; 71 ():926-938.
Chicago/Turabian StyleNeeraj Bokde; Andrés Feijóo; Kishore Kulat. 2018. "Analysis of differencing and decomposition preprocessing methods for wind speed prediction." Applied Soft Computing 71, no. : 926-938.
In recent years logistic functions have been used to model wind turbine power curves. Generally speaking, it can be said that the results provided by the logistic functions are good enough to choose them over other options considering its continuity and adaptability. However, there are some logistic functions that have never been used to model wind turbine power curves although their use can be adequate. Comparing all logistic functions can help definitely to decide which are the best options. In this paper, the most known logistic functions are presented and tested to model wind turbine power curves, included those already used. Moreover, a comparison is made among them, after which two logistic functions are eventually recommended and some other are definitively discarded.
Daniel Villanueva; Andrés Feijóo. Comparison of logistic functions for modeling wind turbine power curves. Electric Power Systems Research 2018, 155, 281 -288.
AMA StyleDaniel Villanueva, Andrés Feijóo. Comparison of logistic functions for modeling wind turbine power curves. Electric Power Systems Research. 2018; 155 ():281-288.
Chicago/Turabian StyleDaniel Villanueva; Andrés Feijóo. 2018. "Comparison of logistic functions for modeling wind turbine power curves." Electric Power Systems Research 155, no. : 281-288.
This letter is an additional contribution to the calculation of wind farm power curves and power probability density functions with the help of the logistic function and Jensens model for wake effect calculation. The goal is to complete a previous formulation based on the use of the 3P-DP logistic function, with two other, called the 4P-DS and the 4P-DP ones.
Andres Feijoo; Daniel Villanueva. Four-Parameter Models for Wind Farm Power Curves and Power Probability Density Functions. IEEE Transactions on Sustainable Energy 2017, 8, 1783 -1784.
AMA StyleAndres Feijoo, Daniel Villanueva. Four-Parameter Models for Wind Farm Power Curves and Power Probability Density Functions. IEEE Transactions on Sustainable Energy. 2017; 8 (4):1783-1784.
Chicago/Turabian StyleAndres Feijoo; Daniel Villanueva. 2017. "Four-Parameter Models for Wind Farm Power Curves and Power Probability Density Functions." IEEE Transactions on Sustainable Energy 8, no. 4: 1783-1784.