This page has only limited features, please log in for full access.
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.
Claudio Monteiro; L. Alfredo Fernandez-Jimenez; Ignacio J. Ramirez-Rosado. Predictive Trading Strategy for Physical Electricity Futures. Energies 2020, 13, 3555 .
AMA StyleClaudio Monteiro, L. Alfredo Fernandez-Jimenez, Ignacio J. Ramirez-Rosado. Predictive Trading Strategy for Physical Electricity Futures. Energies. 2020; 13 (14):3555.
Chicago/Turabian StyleClaudio Monteiro; L. Alfredo Fernandez-Jimenez; Ignacio J. Ramirez-Rosado. 2020. "Predictive Trading Strategy for Physical Electricity Futures." Energies 13, no. 14: 3555.
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 StyleCláudio 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 StyleCláudio 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.
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.
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 StyleAna 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 StyleAna 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.
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.
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 StyleClaudio 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 StyleClaudio 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.
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 StyleRodolfo 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 StyleRodolfo 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.
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.
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 StyleClaudio 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 StyleClaudio 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.
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.
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 StyleCláudio 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 StyleCláudio 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.
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 StyleCláudio 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 StyleCláudio 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.
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.
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 StyleCláudio 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 StyleCláudio 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.
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.
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 StyleClaudio 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 StyleClaudio 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.
Cláudio Monteiro; Ignacio Juan Ramírez Rosado; L. Alfredo Fernandez-Jimenez. Short-term forecasting model for electric power production of small-hydro power plants. Renewable Energy 2013, 50, 387 -394.
AMA StyleCláudio Monteiro, Ignacio Juan Ramírez Rosado, L. Alfredo Fernandez-Jimenez. Short-term forecasting model for electric power production of small-hydro power plants. Renewable Energy. 2013; 50 ():387-394.
Chicago/Turabian StyleCláudio Monteiro; Ignacio Juan Ramírez Rosado; L. Alfredo Fernandez-Jimenez. 2013. "Short-term forecasting model for electric power production of small-hydro power plants." Renewable Energy 50, no. : 387-394.
For the power distribution systems planner it is essential to know the best number and in which power network sections, the control and protective devices must be placed. This paper presents a method for determining the optimal number and the optimal locations of sectionalizing switches and protective devices, in order to achieve a reliability improvement of the distribution network systems and to reduce the investment cost. Thus, a multi-objective nonlinear optimization model was used for determining all the non-dominated planning solutions (Pareto’s solutions) obtained from the optimization of two objective functions: a) objective function of reliability represented by ASIDI, ASIFI and ENS indexes; and b) objective function of economic investment costs. The constraints are considered taking into account technical and economic limitations imposed by the electric company (i.e. coordination of protective devices and sections prohibited for some devices).
Ignacio Juan Ramírez Rosado; Enrique Zorzano-Alba. Optimizing the Number and Location of Switching and Protective Devices in Power Distribution Networks. Lecture Notes in Electrical Engineering 2013, 309 -316.
AMA StyleIgnacio Juan Ramírez Rosado, Enrique Zorzano-Alba. Optimizing the Number and Location of Switching and Protective Devices in Power Distribution Networks. Lecture Notes in Electrical Engineering. 2013; ():309-316.
Chicago/Turabian StyleIgnacio Juan Ramírez Rosado; Enrique Zorzano-Alba. 2013. "Optimizing the Number and Location of Switching and Protective Devices in Power Distribution Networks." Lecture Notes in Electrical Engineering , no. : 309-316.
This paper presents a multi-objective model for planning studies of power distribution systems. The methodology determines optimal economic costs, “pollution”, reliability, and power losses in electric power distribution networks expansion for optimal integration of alternative dispersed generation sources and energy storage systems. The proposed optimal multi-objective planning model can be very useful in determining the most satisfactory selection of investments to meet new energetic, environmental and electric service quality targets from the point of view of new energy policies. This model was tested on a part of the electric power distribution system of La Rioja (Spain).
I. J. Ramírez-Rosado; Eduardo García-Garrido. Multi-objective Model for Optimal Integration of Dispersed Generation and Energy Storage Systems in Electric Power Distribution Networks Expansion. Lecture Notes in Electrical Engineering 2012, 177, 497 -504.
AMA StyleI. J. Ramírez-Rosado, Eduardo García-Garrido. Multi-objective Model for Optimal Integration of Dispersed Generation and Energy Storage Systems in Electric Power Distribution Networks Expansion. Lecture Notes in Electrical Engineering. 2012; 177 ():497-504.
Chicago/Turabian StyleI. J. Ramírez-Rosado; Eduardo García-Garrido. 2012. "Multi-objective Model for Optimal Integration of Dispersed Generation and Energy Storage Systems in Electric Power Distribution Networks Expansion." Lecture Notes in Electrical Engineering 177, no. : 497-504.
This paper presents a novel and useful GIS model for evaluation of electric power generation from the solar resource available in a given region, through the creation of a comprehensive geographical database in a Geographic Information System (GIS). A large amount of data from various sources (weather, reflectance, technologies, etc.) are subjected to detailed calculations which lead to the evaluation of specific local characteristics of power generation (during a typical period of time: typical month, typical year) at each point of the region under study. It has been applied to the Spanish region of La Rioja, divided into cells of GIS coverage of 5x5 meters, a resolution never used before (more than 1012 points studied). The model is applicable to any resolution and any area where reliable meteorological and geographical data can be collected.
Ignacio J. Ramirez-Rosado; Pedro J. Zorzano-Santamaría; Pedro J. Zorzano. A Geographic Information System Model for Evaluation of Electric Power Generation from Photovoltaic Installations. Lecture Notes in Electrical Engineering 2012, 177, 489 -496.
AMA StyleIgnacio J. Ramirez-Rosado, Pedro J. Zorzano-Santamaría, Pedro J. Zorzano. A Geographic Information System Model for Evaluation of Electric Power Generation from Photovoltaic Installations. Lecture Notes in Electrical Engineering. 2012; 177 ():489-496.
Chicago/Turabian StyleIgnacio J. Ramirez-Rosado; Pedro J. Zorzano-Santamaría; Pedro J. Zorzano. 2012. "A Geographic Information System Model for Evaluation of Electric Power Generation from Photovoltaic Installations." Lecture Notes in Electrical Engineering 177, no. : 489-496.
Highlights ► Spatiotemporal future expansion of small power photovoltaic systems in a region. ► Geographically disaggregated representation of future power PV systems expansion. ► Results can be valuable for electric utilities, investors and regional authorities.
Ignacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez; Cláudio Monteiro; Eduardo Garcia-Garrido; Pedro Zorzano-Santamaria; Pedro J. Zorzano. Spatial long-term forecasting of small power photovoltaic systems expansion. Renewable Energy 2011, 36, 3499 -3506.
AMA StyleIgnacio J. Ramirez-Rosado, L. Alfredo Fernandez-Jimenez, Cláudio Monteiro, Eduardo Garcia-Garrido, Pedro Zorzano-Santamaria, Pedro J. Zorzano. Spatial long-term forecasting of small power photovoltaic systems expansion. Renewable Energy. 2011; 36 (12):3499-3506.
Chicago/Turabian StyleIgnacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez; Cláudio Monteiro; Eduardo Garcia-Garrido; Pedro Zorzano-Santamaria; Pedro J. Zorzano. 2011. "Spatial long-term forecasting of small power photovoltaic systems expansion." Renewable Energy 36, no. 12: 3499-3506.
This paper describes an application of the Strength Pareto Evolutionary Algorithm to the multi-objective optimization of a stand-alone PV–wind–diesel system with batteries storage. The objectives to be minimized are the levelized cost of energy (LCOE) and the equivalent carbon dioxide (CO2) life cycle emissions (LCE). Each solution of the Pareto front is a possible solution for the PV–wind–diesel-batteries system, which can supply the load, but each one has a different LCOE and LCE. Some solutions have low LCOE values, but high LCE values, and vice versa. Results show that the photovoltaic (PV) generator is the most important source of electrical energy for stand-alone systems in Spain and Southern Europe, not only environmentally, but also economically. In some cases, PV is almost the only source of energy, as some solutions of the best Pareto front do not include wind turbines and diesel generators run only a few hours during the year.
Rodolfo Dufo-López; José L. Bernal-Agustín; Jose M. Yusta; José A. Domínguez-Navarro; Ignacio J. Ramírez-Rosado; Juan Lujano; Ismael Aso. Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage. Applied Energy 2011, 88, 4033 -4041.
AMA StyleRodolfo Dufo-López, José L. Bernal-Agustín, Jose M. Yusta, José A. Domínguez-Navarro, Ignacio J. Ramírez-Rosado, Juan Lujano, Ismael Aso. Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage. Applied Energy. 2011; 88 (11):4033-4041.
Chicago/Turabian StyleRodolfo Dufo-López; José L. Bernal-Agustín; Jose M. Yusta; José A. Domínguez-Navarro; Ignacio J. Ramírez-Rosado; Juan Lujano; Ismael Aso. 2011. "Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage." Applied Energy 88, no. 11: 4033-4041.
L.A. Fernez-Jimenez; I.J. Ramirez-Rosado; B. Abebe; M. Mendoza-Villena. Wavelet Decomposition and Neuro-Fuzzy Hybrid System Applied to Short-Term Wind Power Forecasting. Modelling, Identification, and Control 2010, 1 .
AMA StyleL.A. Fernez-Jimenez, I.J. Ramirez-Rosado, B. Abebe, M. Mendoza-Villena. Wavelet Decomposition and Neuro-Fuzzy Hybrid System Applied to Short-Term Wind Power Forecasting. Modelling, Identification, and Control. 2010; ():1.
Chicago/Turabian StyleL.A. Fernez-Jimenez; I.J. Ramirez-Rosado; B. Abebe; M. Mendoza-Villena. 2010. "Wavelet Decomposition and Neuro-Fuzzy Hybrid System Applied to Short-Term Wind Power Forecasting." Modelling, Identification, and Control , no. : 1.
This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning.
Ignacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez; Cláudio Monteiro; João Sousa; Ricardo Bessa. Comparison of two new short-term wind-power forecasting systems. Renewable Energy 2009, 34, 1848 -1854.
AMA StyleIgnacio J. Ramirez-Rosado, L. Alfredo Fernandez-Jimenez, Cláudio Monteiro, João Sousa, Ricardo Bessa. Comparison of two new short-term wind-power forecasting systems. Renewable Energy. 2009; 34 (7):1848-1854.
Chicago/Turabian StyleIgnacio J. Ramirez-Rosado; L. Alfredo Fernandez-Jimenez; Cláudio Monteiro; João Sousa; Ricardo Bessa. 2009. "Comparison of two new short-term wind-power forecasting systems." Renewable Energy 34, no. 7: 1848-1854.
The integration in electric power networks of new renewable energy facilities is the final result of a complex planning process. One of the important objectives of this process is the selection of suitable geographical locations where such facilities can be built. This selection procedure can be a difficult task because of the initially opposing positions of the different agents involved in this procedure, such as, for example, investors, utilities, governmental agencies or social groups. The conflicting interest of the agents can delay or block the construction of new facilities. This paper presents a new decision support system, based on Geographic Information Systems, designed to overcome the problems posed by the agents and thus achieve a consensual selection of locations and overcome the problems deriving from their preliminary differing preferences. This paper presents the description of the decision support system, as well as the results obtained for two groups of agents useful for the selection of locations for the construction of new wind farms in La Rioja (Spain).
Ignacio J. Ramírez-Rosado; Eduardo García-Garrido; L. Alfredo Fernandez-Jimenez; Pedro J. Zorzano; Cláudio Monteiro; Vladimiro Miranda. Promotion of new wind farms based on a decision support system. Renewable Energy 2008, 33, 558 -566.
AMA StyleIgnacio J. Ramírez-Rosado, Eduardo García-Garrido, L. Alfredo Fernandez-Jimenez, Pedro J. Zorzano, Cláudio Monteiro, Vladimiro Miranda. Promotion of new wind farms based on a decision support system. Renewable Energy. 2008; 33 (4):558-566.
Chicago/Turabian StyleIgnacio J. Ramírez-Rosado; Eduardo García-Garrido; L. Alfredo Fernandez-Jimenez; Pedro J. Zorzano; Cláudio Monteiro; Vladimiro Miranda. 2008. "Promotion of new wind farms based on a decision support system." Renewable Energy 33, no. 4: 558-566.
Jose M. Yusta; Ignacio Juan Ramírez Rosado; J.A. Dominguez-Navarro; J.M. Perez-Vidal. Optimal electricity price calculation model for retailers in a deregulated market. International Journal of Electrical Power & Energy Systems 2005, 27, 437 -447.
AMA StyleJose M. Yusta, Ignacio Juan Ramírez Rosado, J.A. Dominguez-Navarro, J.M. Perez-Vidal. Optimal electricity price calculation model for retailers in a deregulated market. International Journal of Electrical Power & Energy Systems. 2005; 27 (5-6):437-447.
Chicago/Turabian StyleJose M. Yusta; Ignacio Juan Ramírez Rosado; J.A. Dominguez-Navarro; J.M. Perez-Vidal. 2005. "Optimal electricity price calculation model for retailers in a deregulated market." International Journal of Electrical Power & Energy Systems 27, no. 5-6: 437-447.