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Dr. L. Alfredo Fernandez-Jimenez
Department of Electrical Engineering, Universidad de La Rioja, La Rioja, Spain

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Research Keywords & Expertise

0 Electricity Markets
0 Renewables
0 Grid integration of distributed energy systems
0 Energy forecasting models
0 Power systems planning

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Renewables

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Journal article
Published: 28 April 2021 in Utilities Policy
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Demand Response (DR) is an opportunity and a concern for markets as well as power system flexibility. The deployment of DR depends on both knowledge on its performance and how to measure it effectively to provide adequate economic feedback. DR verification requires a baseline reference. This paper introduces a new baseline that provides an evaluation of response based on simple adjustment factors through physically-based models, tools which are also used in DR. The approach includes the detection of licit and gaming responses before and after DR. Results show that errors decrease by 10–15% with respect to conventional approaches.

ACS Style

A. Gabaldón; A. García-Garre; M.C. Ruiz-Abellón; A. Guillamón; C. Álvarez-Bel; L.A. Fernandez-Jimenez. Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models. Utilities Policy 2021, 70, 101213 .

AMA Style

A. Gabaldón, A. García-Garre, M.C. Ruiz-Abellón, A. Guillamón, C. Álvarez-Bel, L.A. Fernandez-Jimenez. Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models. Utilities Policy. 2021; 70 ():101213.

Chicago/Turabian Style

A. Gabaldón; A. García-Garre; M.C. Ruiz-Abellón; A. Guillamón; C. Álvarez-Bel; L.A. Fernandez-Jimenez. 2021. "Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models." Utilities Policy 70, no. : 101213.

Journal article
Published: 10 July 2020 in Energies
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This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading.

ACS Style

Claudio Monteiro; L. Alfredo Fernandez-Jimenez; Ignacio J. Ramirez-Rosado. Predictive Trading Strategy for Physical Electricity Futures. Energies 2020, 13, 3555 .

AMA Style

Claudio Monteiro, L. Alfredo Fernandez-Jimenez, Ignacio J. Ramirez-Rosado. Predictive Trading Strategy for Physical Electricity Futures. Energies. 2020; 13 (14):3555.

Chicago/Turabian Style

Claudio Monteiro; L. Alfredo Fernandez-Jimenez; Ignacio J. Ramirez-Rosado. 2020. "Predictive Trading Strategy for Physical Electricity Futures." Energies 13, no. 14: 3555.

Conference paper
Published: 14 February 2020 in E3S Web of Conferences
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This paper presents an original probabilistic photovoltaic (PV) power forecasting model for the day-ahead hourly generation in a PV plant. The probabilistic forecasting model is based on 12 deterministic models developed with different techniques. An optimization process, ruled by a genetic algorithm, chooses the forecasts of the deterministic models in order to achieve the probability distribution function (PDF) for the PV generation in each one of the daylight hours of the following day in a parametric approach. The PDFs, which constitute the probabilistic forecasts, are a mixture of normal distributions, each one centred in the forecasts of the selected deterministic models. The genetic algorithm chooses the deterministic forecasts, the variance of the normal distributions and their weights in the mixture. In a case study the proposed model achieves better forecasting results than the obtained with the conditional quantile regression method applied to the same data used to develop the deterministic forecasting models.

ACS Style

L. Alfredo Fernandez-Jimenez; Sonia Terreros-Olarte; Pedro J. Zorzano-Santamaria; Montserrat Mendoza-Villena; Eduardo Garcia-Garrido. Probabilistic photovoltaic power forecasting model based on deterministic forecasts. E3S Web of Conferences 2020, 152, 01003 .

AMA Style

L. Alfredo Fernandez-Jimenez, Sonia Terreros-Olarte, Pedro J. Zorzano-Santamaria, Montserrat Mendoza-Villena, Eduardo Garcia-Garrido. Probabilistic photovoltaic power forecasting model based on deterministic forecasts. E3S Web of Conferences. 2020; 152 ():01003.

Chicago/Turabian Style

L. Alfredo Fernandez-Jimenez; Sonia Terreros-Olarte; Pedro J. Zorzano-Santamaria; Montserrat Mendoza-Villena; Eduardo Garcia-Garrido. 2020. "Probabilistic photovoltaic power forecasting model based on deterministic forecasts." E3S Web of Conferences 152, no. : 01003.

Conference paper
Published: 14 February 2020 in E3S Web of Conferences
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This paper presents an original trading strategy for electricity buyers in futures markets. The strategy applies a medium-term electricity price forecasting model to predict the monthly average spot price which is used to evaluate the Risk Premium for a physical delivery under a monthly electricity futures contract. The proposed trading strategy aims to provide an advantage relatively to the traditional strategy of electricity buyers (used as benchmark), anticipating the good/wrong decision of buying electricity in the futures market instead in the day-ahead market. The mid-term monthly average spot price forecasting model, which supports the trading strategy, uses only information available from futures and spot markets at the decision moment. Both the new trading strategy and the monthly average spot price forecasting model, proposed in this paper, have been successfully tested with historical data of the Iberian Electricity Market (MIBEL), although they could be applied to other electricity markets.

ACS Style

Cláudio Monteiro; Ignacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez. A strategy for electricity buyers in futures markets. E3S Web of Conferences 2020, 152, 03007 .

AMA Style

Cláudio Monteiro, Ignacio J. Ramirez-Rosado, L. Alfredo Fernandez-Jimenez. A strategy for electricity buyers in futures markets. E3S Web of Conferences. 2020; 152 ():03007.

Chicago/Turabian Style

Cláudio Monteiro; Ignacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez. 2020. "A strategy for electricity buyers in futures markets." E3S Web of Conferences 152, no. : 03007.

Conference paper
Published: 14 February 2020 in E3S Web of Conferences
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This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature.

ACS Style

L. Alfredo Fernandez-Jimenez; Sonia Terreros-Olarte; Alberto Falces; Pedro M. Lara-Santillan; Enrique Zorzano-Alba; Pedro J. Zorzano-Santamaria. Probabilistic reference model for hourly PV power generation forecasting. E3S Web of Conferences 2020, 152, 01002 .

AMA Style

L. Alfredo Fernandez-Jimenez, Sonia Terreros-Olarte, Alberto Falces, Pedro M. Lara-Santillan, Enrique Zorzano-Alba, Pedro J. Zorzano-Santamaria. Probabilistic reference model for hourly PV power generation forecasting. E3S Web of Conferences. 2020; 152 ():01002.

Chicago/Turabian Style

L. Alfredo Fernandez-Jimenez; Sonia Terreros-Olarte; Alberto Falces; Pedro M. Lara-Santillan; Enrique Zorzano-Alba; Pedro J. Zorzano-Santamaria. 2020. "Probabilistic reference model for hourly PV power generation forecasting." E3S Web of Conferences 152, no. : 01002.

Journal article
Published: 01 December 2018 in International Journal of Electrical Power & Energy Systems
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ACS Style

Cláudio Monteiro; Ignacio Juan Ramírez Rosado; L. Alfredo Fernandez-Jimenez; Miguel Ribeiro. New probabilistic price forecasting models: Application to the Iberian electricity market. International Journal of Electrical Power & Energy Systems 2018, 103, 483 -496.

AMA Style

Cláudio Monteiro, Ignacio Juan Ramírez Rosado, L. Alfredo Fernandez-Jimenez, Miguel Ribeiro. New probabilistic price forecasting models: Application to the Iberian electricity market. International Journal of Electrical Power & Energy Systems. 2018; 103 ():483-496.

Chicago/Turabian Style

Cláudio Monteiro; Ignacio Juan Ramírez Rosado; L. Alfredo Fernandez-Jimenez; Miguel Ribeiro. 2018. "New probabilistic price forecasting models: Application to the Iberian electricity market." International Journal of Electrical Power & Energy Systems 103, no. : 483-496.

Conference paper
Published: 01 September 2018 in 2018 International Conference on Smart Energy Systems and Technologies (SEST)
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Buildings are an important segment from the point of view of the overall consumption and the flexibility or change in their demand through Demand Response, Energy Efficiency and Renewable Sources. The integration of renewables represents an opportunity for buildings because main end-uses (for example heat, cool, and ventilation) follow, at some extend, the potential of solar resource. The problem is that renewable resources are evaluated through simulators from average conditions (irradiation, external temperature) but in practice, and in the short term, the generation resource exhibits an important volatility and, in some cases, the integration of renewables can produce not only benefits but risks for the customer from an economic aspect. The aim of this paper is the evaluation of these risks, and to state how Demand Response policies and, of course, Renewable Energy Sources (RES) models, can help to reduce or mitigate these risks and volatility. A real university building is presented to exemplify the methodology.

ACS Style

Ana Garcia-Garre; Antonio Gabaldon; Luis A. Fernandez-Jimenez; Carlos Alvarez-Bel; Ignacio J. Ramirez-Rosado; Sergio Valero-Verdu; Carolina Senabre. Evaluation and Integration of Demand Response and Photovoltaic Generation in Institutional Buildings. 2018 International Conference on Smart Energy Systems and Technologies (SEST) 2018, 1 -6.

AMA Style

Ana Garcia-Garre, Antonio Gabaldon, Luis A. Fernandez-Jimenez, Carlos Alvarez-Bel, Ignacio J. Ramirez-Rosado, Sergio Valero-Verdu, Carolina Senabre. Evaluation and Integration of Demand Response and Photovoltaic Generation in Institutional Buildings. 2018 International Conference on Smart Energy Systems and Technologies (SEST). 2018; ():1-6.

Chicago/Turabian Style

Ana Garcia-Garre; Antonio Gabaldon; Luis A. Fernandez-Jimenez; Carlos Alvarez-Bel; Ignacio J. Ramirez-Rosado; Sergio Valero-Verdu; Carolina Senabre. 2018. "Evaluation and Integration of Demand Response and Photovoltaic Generation in Institutional Buildings." 2018 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.

Journal article
Published: 26 April 2018 in Energies
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This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF) of a Beta distribution for the output variable (hourly price) can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI) and a Loss function Indicator (LI) are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

ACS Style

Claudio Monteiro; Ignacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez. Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors. Energies 2018, 11, 1074 .

AMA Style

Claudio Monteiro, Ignacio J. Ramirez-Rosado, L. Alfredo Fernandez-Jimenez. Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors. Energies. 2018; 11 (5):1074.

Chicago/Turabian Style

Claudio Monteiro; Ignacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez. 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors." Energies 11, no. 5: 1074.

Journal article
Published: 01 December 2017 in Solar Energy
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Traditionally, the accuracy of solar power forecasts has been measured in terms of classic metrics, such as root mean square error (RMSE) or mean absolute error (MAE), and it is widely accepted that the smaller the error, the greater the economic benefits. Nevertheless, this is not as straightforward as it may seem, because market conditions must be studied first. Relationships between magnitudes of deviations between forecast and actual production and market penalties that apply at each moment are crucial. In this study, we analyze various day-ahead production forecasts for a 1.86 MW photovoltaic plant considering different techniques and sets of inputs. A nRMSE of 22.54% was obtained for a Support Vector Regression model trained by numerical weather predictions (NWP). This model produced the most benefits. An annual forecasting value of 4788€ with respect to a persistence model was obtained for trading in the Iberian (Spain and Portugal) day-ahead electricity market. Annual value added by the NWP service totaled 2801€ and room for improvement regarding NWP variables rose to 3877€. As a general trend, it was found that smaller errors (RMSE) generated higher incomes. For each 1 kW h improvement in RMSE, the annual value of forecasting increased 22.32€. Nevertheless, some models that gave larger errors than others also brought greater benefits. Thus, market conditions must be considered to accurately evaluate model economic performance.

ACS Style

J. Antonanzas; David Pozo-Vazquez; L. Alfredo Fernandez-Jimenez; F.J. Martinez-De-Pison. The value of day-ahead forecasting for photovoltaics in the Spanish electricity market. Solar Energy 2017, 158, 140 -146.

AMA Style

J. Antonanzas, David Pozo-Vazquez, L. Alfredo Fernandez-Jimenez, F.J. Martinez-De-Pison. The value of day-ahead forecasting for photovoltaics in the Spanish electricity market. Solar Energy. 2017; 158 ():140-146.

Chicago/Turabian Style

J. Antonanzas; David Pozo-Vazquez; L. Alfredo Fernandez-Jimenez; F.J. Martinez-De-Pison. 2017. "The value of day-ahead forecasting for photovoltaics in the Spanish electricity market." Solar Energy 158, no. : 140-146.

Conference paper
Published: 01 November 2017 in 2017 European Conference on Electrical Engineering and Computer Science (EECS)
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The expansion of electric distribution networks in new geographic areas is a tedious task. Once the position of the low voltage power substations has been decided, the planning engineers need to select the routes for the new power lines ensuring more efficient connections among the substations. This paper presents the methodology followed to plan the set of overhead power lines which achieves the optimal distribution network with the minimum installation and maintenance costs. The methodology is based on the use of Geographic Information Systems, which provide the needed functions to find feasible and economic routes for the new overhead power lines linking the substations, and an evolutionary algorithm which selects the optimal links. The application of the proposed methodology allows finding the optimal solution under an economic perspective in an automatic manner.

ACS Style

Eduardo Garcia-Garrido; L. Alfredo Fernandez-Jimenez; Montserrat Mendoza-Villena; Pedro M. Lara-Santillan; Pedro J. Zorzano-Santamaria; Enrique Zorzano-Alba; Alberto Falces. Electric Power Distribution Planning Tool Based on Geographic Information Systems and Evolutionary Algorithms. 2017 European Conference on Electrical Engineering and Computer Science (EECS) 2017, 396 -401.

AMA Style

Eduardo Garcia-Garrido, L. Alfredo Fernandez-Jimenez, Montserrat Mendoza-Villena, Pedro M. Lara-Santillan, Pedro J. Zorzano-Santamaria, Enrique Zorzano-Alba, Alberto Falces. Electric Power Distribution Planning Tool Based on Geographic Information Systems and Evolutionary Algorithms. 2017 European Conference on Electrical Engineering and Computer Science (EECS). 2017; ():396-401.

Chicago/Turabian Style

Eduardo Garcia-Garrido; L. Alfredo Fernandez-Jimenez; Montserrat Mendoza-Villena; Pedro M. Lara-Santillan; Pedro J. Zorzano-Santamaria; Enrique Zorzano-Alba; Alberto Falces. 2017. "Electric Power Distribution Planning Tool Based on Geographic Information Systems and Evolutionary Algorithms." 2017 European Conference on Electrical Engineering and Computer Science (EECS) , no. : 396-401.

Journal article
Published: 12 October 2017 in Energies
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The construction of new high voltage overhead power lines (HVOPLs) has become a controversial issue for electricity companies due to social opposition. Citizens are concerned about how these power lines may have an impact on their lives, basically caused by their effects on health and safety. Visual impact is one of the most easily perceived. Although there are several published works that deal with the assessment of the visual impact produced by HVOPLs, no methodology has been proposed to assess this impact from an objective perspective. This work presents an original methodology which helps to identify the optimal routes for a new HVOPL under an objective observability criterion, enabling the selection of those with the lowest visibility in a zone. The application of the proposed methodology achieves a set of routes that links new HVOPL origin and destination points creating a corridor which includes all possible routes with an observability of its towers under a threshold limit. This methodology is illustrated by a real-life use corresponding to the selection of the route with least observability for a new power line in La Rioja (Spain). The results obtained may help to achieve a consensus between key stakeholders since it is focused on the specific issues of the planned HVOPL and its observability from an objective perspective.

ACS Style

L. Alfredo Fernandez-Jimenez; Montserrat Mendoza-Villena; Eduardo Garcia-Garrido; Pedro M. Lara-Santillan; Pedro J. Zorzano-Santamaria; Enrique Zorzano-Alba; Alberto Falces. High Voltage Overhead Power Line Routing under an Objective Observability Criterion. Energies 2017, 10, 1576 .

AMA Style

L. Alfredo Fernandez-Jimenez, Montserrat Mendoza-Villena, Eduardo Garcia-Garrido, Pedro M. Lara-Santillan, Pedro J. Zorzano-Santamaria, Enrique Zorzano-Alba, Alberto Falces. High Voltage Overhead Power Line Routing under an Objective Observability Criterion. Energies. 2017; 10 (10):1576.

Chicago/Turabian Style

L. Alfredo Fernandez-Jimenez; Montserrat Mendoza-Villena; Eduardo Garcia-Garrido; Pedro M. Lara-Santillan; Pedro J. Zorzano-Santamaria; Enrique Zorzano-Alba; Alberto Falces. 2017. "High Voltage Overhead Power Line Routing under an Objective Observability Criterion." Energies 10, no. 10: 1576.

Conference paper
Published: 02 June 2017 in Computer Vision
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Solar power forecasts are gaining continuous importance as the penetration of solar energy into the grid rises. The natural variability of the solar resource, joined to the difficulties of cloud movement modeling, endow solar power forecasts with a certain level of uncertainty. Important efforts have been carried out in the field to reduce as much as possible the errors. Various approaches have been followed, being the predominant nowadays the use of statistical techniques to model production. In this study, we have performed a comparison study between two extensively used statistical techniques, support vector regression (SVR) machines and random forests, and two other techniques that have been scarcely applied to solar forecasting, deep neural networks and extreme gradient boosting machines. Best results were obtained with the SVR technique, showing a nRMSE of 22.49%. To complete the assessment, a weighted blended model consisting on an average weighted combination of individual predictions was created. This blended model outperformed all the models studied, with a nRMSE of 22.24%.

ACS Style

Javier Antonanzas; Ruben Urraca; Alpha Pernia-Espinoza; Alvaro Aldama; L. Alfredo Fernandez-Jimenez; Francisco Javier Martínez-De-Pisón. Single and Blended Models for Day-Ahead Photovoltaic Power Forecasting. Computer Vision 2017, 427 -434.

AMA Style

Javier Antonanzas, Ruben Urraca, Alpha Pernia-Espinoza, Alvaro Aldama, L. Alfredo Fernandez-Jimenez, Francisco Javier Martínez-De-Pisón. Single and Blended Models for Day-Ahead Photovoltaic Power Forecasting. Computer Vision. 2017; ():427-434.

Chicago/Turabian Style

Javier Antonanzas; Ruben Urraca; Alpha Pernia-Espinoza; Alvaro Aldama; L. Alfredo Fernandez-Jimenez; Francisco Javier Martínez-De-Pisón. 2017. "Single and Blended Models for Day-Ahead Photovoltaic Power Forecasting." Computer Vision , no. : 427-434.

Journal article
Published: 01 February 2017 in Energy Conversion and Management
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ACS Style

Rodolfo Dufo-López; L. Alfredo Fernandez-Jimenez; Ignacio Juan Ramírez Rosado; Jesús Sergio Artal Sevil; José A. Domínguez-Navarro; José L. Bernal-Agustín. Daily operation optimisation of hybrid stand-alone system by model predictive control considering ageing model. Energy Conversion and Management 2017, 134, 167 -177.

AMA Style

Rodolfo Dufo-López, L. Alfredo Fernandez-Jimenez, Ignacio Juan Ramírez Rosado, Jesús Sergio Artal Sevil, José A. Domínguez-Navarro, José L. Bernal-Agustín. Daily operation optimisation of hybrid stand-alone system by model predictive control considering ageing model. Energy Conversion and Management. 2017; 134 ():167-177.

Chicago/Turabian Style

Rodolfo Dufo-López; L. Alfredo Fernandez-Jimenez; Ignacio Juan Ramírez Rosado; Jesús Sergio Artal Sevil; José A. Domínguez-Navarro; José L. Bernal-Agustín. 2017. "Daily operation optimisation of hybrid stand-alone system by model predictive control considering ageing model." Energy Conversion and Management 134, no. : 167-177.

Journal article
Published: 07 September 2016 in Energies
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This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

ACS Style

Claudio Monteiro; Ignacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez; Pedro Conde. Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market. Energies 2016, 9, 721 .

AMA Style

Claudio Monteiro, Ignacio J. Ramirez-Rosado, L. Alfredo Fernandez-Jimenez, Pedro Conde. Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market. Energies. 2016; 9 (9):721.

Chicago/Turabian Style

Claudio Monteiro; Ignacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez; Pedro Conde. 2016. "Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market." Energies 9, no. 9: 721.

Journal article
Published: 22 September 2015 in Energies
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This paper presents the analysis of the importance of a set of explanatory (input) variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL). The available input variables include extensive hourly time series records of weather forecasts, previous prices, and regional aggregation of power generations and power demands. The paper presents the comparisons of the forecasting results achieved with a model which includes all these available input variables (EMPF model) with respect to those obtained by other forecasting models containing a reduced set of input variables. These comparisons identify the most important variables for forecasting purposes. In addition, a novel Reference Explanatory Model for Price Estimations (REMPE) that achieves hourly price estimations by using actual power generations and power demands of such day is described in the paper, which offers the lowest limit for the forecasting error of the EMPF model. All the models have been implemented using the same technique (artificial neural networks) and have been satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). The relative importance of each explanatory variable is identified for the day-ahead price forecasts in the MIBEL. The comparisons also allow outlining guidelines of the value of the different types of input information.

ACS Style

Cláudio Monteiro; L. Alfredo Fernandez-Jimenez; Ignacio J. Ramirez-Rosado. Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market. Energies 2015, 8, 10464 -10486.

AMA Style

Cláudio Monteiro, L. Alfredo Fernandez-Jimenez, Ignacio J. Ramirez-Rosado. Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market. Energies. 2015; 8 (9):10464-10486.

Chicago/Turabian Style

Cláudio Monteiro; L. Alfredo Fernandez-Jimenez; Ignacio J. Ramirez-Rosado. 2015. "Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market." Energies 8, no. 9: 10464-10486.

Journal article
Published: 01 June 2015 in Renewable Energy
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ACS Style

L. Alfredo Fernandez-Jimenez; Montserrat Mendoza-Villena; Pedro Zorzano-Santamaria; Eduardo Garcia-Garrido; Pedro Lara-Santillan; Enrique Zorzano-Alba; Alberto Falces. Site selection for new PV power plants based on their observability. Renewable Energy 2015, 78, 7 -15.

AMA Style

L. Alfredo Fernandez-Jimenez, Montserrat Mendoza-Villena, Pedro Zorzano-Santamaria, Eduardo Garcia-Garrido, Pedro Lara-Santillan, Enrique Zorzano-Alba, Alberto Falces. Site selection for new PV power plants based on their observability. Renewable Energy. 2015; 78 ():7-15.

Chicago/Turabian Style

L. Alfredo Fernandez-Jimenez; Montserrat Mendoza-Villena; Pedro Zorzano-Santamaria; Eduardo Garcia-Garrido; Pedro Lara-Santillan; Enrique Zorzano-Alba; Alberto Falces. 2015. "Site selection for new PV power plants based on their observability." Renewable Energy 78, no. : 7-15.

Journal article
Published: 01 December 2014 in Energy Conversion and Management
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ACS Style

Cláudio Monteiro; Ignacio Juan Ramírez Rosado; L. Alfredo Fernandez-Jimenez. Short-term forecasting model for aggregated regional hydropower generation. Energy Conversion and Management 2014, 88, 231 -238.

AMA Style

Cláudio Monteiro, Ignacio Juan Ramírez Rosado, L. Alfredo Fernandez-Jimenez. Short-term forecasting model for aggregated regional hydropower generation. Energy Conversion and Management. 2014; 88 ():231-238.

Chicago/Turabian Style

Cláudio Monteiro; Ignacio Juan Ramírez Rosado; L. Alfredo Fernandez-Jimenez. 2014. "Short-term forecasting model for aggregated regional hydropower generation." Energy Conversion and Management 88, no. : 231-238.

Research article
Published: 15 April 2014 in The Scientific World Journal
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This paper presents a comparative study of the electricity consumption (EC) in an urban low-voltage substation before and during the economic crisis (2008–2013). This low-voltage substation supplies electric power to near 400 users. The EC was measured for an 11-year period (2002–2012) with a sampling time of 1 minute. The study described in the paper consists of detecting the changes produced in the load curves of this substation along the time due to changes in the behaviour of consumers. The EC was compared using representative curves per time period (precrisis and crisis). These representative curves were obtained after a computational process, which was based on a search for days with similar curves to the curve of a determined (base) date. This similitude was assessed by the proximity on the calendar, day of the week, daylight time, and outdoor temperature. The last selection parameter was the error between the nearest neighbour curves and the base date curve. The obtained representative curves were linearized to determine changes in their structure (maximum and minimum consumption values, duration of the daily time slot, etc.). The results primarily indicate an increase in the EC in the night slot during the summer months in the crisis period.

ACS Style

Pedro María Lara; Montserrat Mendoza-Villena; L. Alfredo Fernandez-Jimenez; Mario Manana Canteli. A Comparative Study of Electric Load Curve Changes in an Urban Low-Voltage Substation in Spain during the Economic Crisis (2008–2013). The Scientific World Journal 2014, 2014, 1 -14.

AMA Style

Pedro María Lara, Montserrat Mendoza-Villena, L. Alfredo Fernandez-Jimenez, Mario Manana Canteli. A Comparative Study of Electric Load Curve Changes in an Urban Low-Voltage Substation in Spain during the Economic Crisis (2008–2013). The Scientific World Journal. 2014; 2014 ():1-14.

Chicago/Turabian Style

Pedro María Lara; Montserrat Mendoza-Villena; L. Alfredo Fernandez-Jimenez; Mario Manana Canteli. 2014. "A Comparative Study of Electric Load Curve Changes in an Urban Low-Voltage Substation in Spain during the Economic Crisis (2008–2013)." The Scientific World Journal 2014, no. : 1-14.

Research article
Published: 21 November 2013 in Mathematical Problems in Engineering
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We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs). The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.

ACS Style

Cláudio Monteiro; L. Alfredo Fernandez-Jimenez; Ignacio Juan Ramírez Rosado; Andres Muñoz-Jimenez; Pedro María Lara. Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques. Mathematical Problems in Engineering 2013, 2013, 1 -9.

AMA Style

Cláudio Monteiro, L. Alfredo Fernandez-Jimenez, Ignacio Juan Ramírez Rosado, Andres Muñoz-Jimenez, Pedro María Lara. Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques. Mathematical Problems in Engineering. 2013; 2013 ():1-9.

Chicago/Turabian Style

Cláudio Monteiro; L. Alfredo Fernandez-Jimenez; Ignacio Juan Ramírez Rosado; Andres Muñoz-Jimenez; Pedro María Lara. 2013. "Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques." Mathematical Problems in Engineering 2013, no. : 1-9.

Journal article
Published: 22 May 2013 in Energies
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This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.

ACS Style

Claudio Monteiro; Tiago Santos; L. Alfredo Fernandez-Jimenez; Ignacio J. Ramirez-Rosado; M. Sonia Terreros-Olarte. Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity. Energies 2013, 6, 2624 -2643.

AMA Style

Claudio Monteiro, Tiago Santos, L. Alfredo Fernandez-Jimenez, Ignacio J. Ramirez-Rosado, M. Sonia Terreros-Olarte. Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity. Energies. 2013; 6 (5):2624-2643.

Chicago/Turabian Style

Claudio Monteiro; Tiago Santos; L. Alfredo Fernandez-Jimenez; Ignacio J. Ramirez-Rosado; M. Sonia Terreros-Olarte. 2013. "Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity." Energies 6, no. 5: 2624-2643.