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Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

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Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

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dc.contributor.author Hernández, Luis es_ES
dc.contributor.author Baladrón Zorita, Carlos es_ES
dc.contributor.author Aguiar Pérez, Javier Manuel es_ES
dc.contributor.author Carro Martínez, Belén es_ES
dc.contributor.author Sanchez-Esguevillas, Antonio es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2014-10-10T16:03:48Z
dc.date.available 2014-10-10T16:03:48Z
dc.date.issued 2013-03
dc.identifier.issn 1996-1073
dc.identifier.uri http://hdl.handle.net/10251/43123
dc.description.abstract Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply. es_ES
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation.ispartof Energies es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject artificial neural network es_ES
dc.subject distributed intelligence es_ES
dc.subject short-term load forecasting es_ES
dc.subject smart grid es_ES
dc.subject microgrid es_ES
dc.subject multilayer perceptron es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/en6031385
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres es_ES
dc.description.bibliographicCitation Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies. 6(3):1385-1408. doi:10.3390/en6031385 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/en6031385 es_ES
dc.description.upvformatpinicio 1385 es_ES
dc.description.upvformatpfin 1408 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.description.issue 3 es_ES
dc.relation.senia 265810
dc.description.references Booklets European Comission. Your Guide to the Lisbon Treaty 2009http://ec.europa.eu/publications/booklets/others/84/en.pdf es_ES
dc.description.references Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.6400446 es_ES
dc.description.references http://ec.europa.eu/energy/technology/set_plan/set_plan_en.htm es_ES
dc.description.references FUTURED—Spanish Technological Platform for Energy Grids Home Pagehttp://www.futured.es/ es_ES
dc.description.references European Technology Platform for Electricity Networks of the Future—SmartGrids ETP Home Pagehttp://www.smartgrids.eu/ es_ES
dc.description.references Kim, H.-M., Kinoshita, T., & Shin, M.-C. (2010). A Multiagent System for Autonomous Operation of Islanded Microgrids Based on a Power Market Environment. Energies, 3(12), 1972-1990. doi:10.3390/en3121972 es_ES
dc.description.references Kim, H.-M., Lim, Y., & Kinoshita, T. (2012). An Intelligent Multiagent System for Autonomous Microgrid Operation. Energies, 5(9), 3347-3362. doi:10.3390/en5093347 es_ES
dc.description.references Xiao, Z., Li, T., Huang, M., Shi, J., Yang, J., Yu, J., & Wu, W. (2010). Hierarchical MAS Based Control Strategy for Microgrid. Energies, 3(9), 1622-1638. doi:10.3390/en3091622 es_ES
dc.description.references Hong, Y.-Y., & Chou, J.-H. (2012). Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks. Energies, 5(7), 2578-2593. doi:10.3390/en5072578 es_ES
dc.description.references Douglas, A. P., Breipohl, A. M., Lee, F. N., & Adapa, R. (1998). The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Transactions on Power Systems, 13(4), 1507-1513. doi:10.1109/59.736298 es_ES
dc.description.references Sadownik, R., & Barbosa, E. P. (1999). Short-term forecasting of industrial electricity consumption in Brazil. Journal of Forecasting, 18(3), 215-224. doi:10.1002/(sici)1099-131x(199905)18:3<215::aid-for719>3.0.co;2-b es_ES
dc.description.references Huang, S. R. (1997). Short-term load forecasting using threshold autoregressive models. IEE Proceedings - Generation, Transmission and Distribution, 144(5), 477. doi:10.1049/ip-gtd:19971144 es_ES
dc.description.references Infield, D. G., & Hill, D. C. (1998). Optimal smoothing for trend removal in short term electricity demand forecasting. IEEE Transactions on Power Systems, 13(3), 1115-1120. doi:10.1109/59.709108 es_ES
dc.description.references Sargunaraj, S., Sen Gupta, D. P., & Devi, S. (1997). Short-term load forecasting for demand side management. IEE Proceedings - Generation, Transmission and Distribution, 144(1), 68. doi:10.1049/ip-gtd:19970599 es_ES
dc.description.references Hong-Tzer Yang, & Chao-Ming Huang. (1998). A new short-term load forecasting approach using self-organizing fuzzy ARMAX models. IEEE Transactions on Power Systems, 13(1), 217-225. doi:10.1109/59.651639 es_ES
dc.description.references Hong-Tzer Yang, Chao-Ming Huang, & Ching-Lien Huang. (1996). Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. IEEE Transactions on Power Systems, 11(1), 403-408. doi:10.1109/59.486125 es_ES
dc.description.references Yu, Z. (1996). A temperature match based optimization method for daily load prediction considering DLC effect. IEEE Transactions on Power Systems, 11(2), 728-733. doi:10.1109/59.496146 es_ES
dc.description.references Charytoniuk, W., Chen, M. S., & Van Olinda, P. (1998). Nonparametric regression based short-term load forecasting. IEEE Transactions on Power Systems, 13(3), 725-730. doi:10.1109/59.708572 es_ES
dc.description.references Taylor, J. W., & Majithia, S. (2000). Using combined forecasts with changing weights for electricity demand profiling. Journal of the Operational Research Society, 51(1), 72-82. doi:10.1057/palgrave.jors.2600856 es_ES
dc.description.references Ramanathan, R., Engle, R., Granger, C. W. J., Vahid-Araghi, F., & Brace, C. (1997). Short-run forecasts of electricity loads and peaks. International Journal of Forecasting, 13(2), 161-174. doi:10.1016/s0169-2070(97)00015-0 es_ES
dc.description.references Soliman, S. A., Persaud, S., El-Nagar, K., & El-Hawary, M. E. (1997). Application of least absolute value parameter estimation based on linear programming to short-term load forecasting. International Journal of Electrical Power & Energy Systems, 19(3), 209-216. doi:10.1016/s0142-0615(96)00048-8 es_ES
dc.description.references Hyde, O., & Hodnett, P. F. (1997). An adaptable automated procedure for short-term electricity load forecasting. IEEE Transactions on Power Systems, 12(1), 84-94. doi:10.1109/59.574927 es_ES
dc.description.references Ku-Long Ho, Yuan-Yih Hsu, Chuan-Fu Chen, Tzong-En Lee, Chih-Chien Liang, Tsau-Shin Lai, & Kung-Keng Chen. (1990). Short term load forecasting of Taiwan power system using a knowledge-based expert system. IEEE Transactions on Power Systems, 5(4), 1214-1221. doi:10.1109/59.99372 es_ES
dc.description.references Rahman, S., & Hazim, O. (1993). A generalized knowledge-based short-term load-forecasting technique. IEEE Transactions on Power Systems, 8(2), 508-514. doi:10.1109/59.260833 es_ES
dc.description.references Mori, H., & Kobayashi, H. (1996). Optimal fuzzy inference for short-term load forecasting. IEEE Transactions on Power Systems, 11(1), 390-396. doi:10.1109/59.486123 es_ES
dc.description.references Bakirtzis, A. G., Theocharis, J. B., Kiartzis, S. J., & Satsios, K. J. (1995). Short term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 10(3), 1518-1524. doi:10.1109/59.466494 es_ES
dc.description.references Papadakis, S. E., Theocharis, J. B., Kiartzis, S. J., & Bakirtzis, A. G. (1998). A novel approach to short-term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 13(2), 480-492. doi:10.1109/59.667372 es_ES
dc.description.references Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179-211. doi:10.1207/s15516709cog1402_1 es_ES
dc.description.references Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2-3), 195-225. doi:10.1007/bf00114844 es_ES
dc.description.references Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480. doi:10.1109/5.58325 es_ES
dc.description.references Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., & Hernández, L. (2012). Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors, 12(2), 1468-1481. doi:10.3390/s120201468 es_ES
dc.description.references Martínez-Martínez, V., Baladrón, C., Gomez-Gil, J., Ruiz-Ruiz, G., Navas-Gracia, L. M., Aguiar, J. M., & Carro, B. (2012). Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks. Sensors, 12(10), 14004-14021. doi:10.3390/s121014004 es_ES
dc.description.references Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., & Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 6(2), 442-449. doi:10.1109/59.76685 es_ES
dc.description.references Ho, K. L., Hsu, Y.-Y., & Yang, C.-C. (1992). Short term load forecasting using a multilayer neural network with an adaptive learning algorithm. IEEE Transactions on Power Systems, 7(1), 141-149. doi:10.1109/59.141697 es_ES
dc.description.references Drezga, I., & Rahman, S. (1998). Input variable selection for ANN-based short-term load forecasting. IEEE Transactions on Power Systems, 13(4), 1238-1244. doi:10.1109/59.736244 es_ES
dc.description.references Drezga, I., & Rahman, S. (1999). Short-term load forecasting with local ANN predictors. IEEE Transactions on Power Systems, 14(3), 844-850. doi:10.1109/59.780894 es_ES
dc.description.references Lee, K. Y., Cha, Y. T., & Park, J. H. (1992). Short-term load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 7(1), 124-132. doi:10.1109/59.141695 es_ES
dc.description.references Lu, C.-N., Wu, H.-T., & Vemuri, S. (1993). Neural network based short term load forecasting. IEEE Transactions on Power Systems, 8(1), 336-342. doi:10.1109/59.221223 es_ES
dc.description.references Papalexopoulos, A. D., Shangyou Hao, & Tie-Mao Peng. (1994). An implementation of a neural network based load forecasting model for the EMS. IEEE Transactions on Power Systems, 9(4), 1956-1962. doi:10.1109/59.331456 es_ES
dc.description.references Bakirtzls, A. G., Petridls, V., Klartzis, S. J., Alexladls, M. C., & Malssls, A. H. (1996). A neural network short term load forecasting model for the Greek power system. IEEE Transactions on Power Systems, 11(2), 858-863. doi:10.1109/59.496166 es_ES
dc.description.references AlFuhaid, A. S., El-Sayed, M. A., & Mahmoud, M. S. (1997). Cascaded artificial neural networks for short-term load forecasting. IEEE Transactions on Power Systems, 12(4), 1524-1529. doi:10.1109/59.627852 es_ES
dc.description.references Lamedica, R., Prudenzi, A., Sforna, M., Caciotta, M., & Cencellli, V. O. (1996). A neural network based technique for short-term forecasting of anomalous load periods. IEEE Transactions on Power Systems, 11(4), 1749-1756. doi:10.1109/59.544638 es_ES
dc.description.references Srinivasan, D. (1994). Forecasting daily load curves using a hybrid fuzzy-neural approach. IEE Proceedings - Generation, Transmission and Distribution, 141(6), 561. doi:10.1049/ip-gtd:19941288 es_ES
dc.description.references Kwang-Ho Kim, Jong-Keun Park, Kab-Ju Hwang, & Sung-Hak Kim. (1995). Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Transactions on Power Systems, 10(3), 1534-1539. doi:10.1109/59.466492 es_ES
dc.description.references Daneshdoost, M., Lotfalian, M., Bumroonggit, G., & Ngoy, J. P. (1998). Neural network with fuzzy set-based classification for short-term load forecasting. IEEE Transactions on Power Systems, 13(4), 1386-1391. doi:10.1109/59.736281 es_ES
dc.description.references Senjyu, T., Mandal, P., Uezato, K., & Funabashi, T. (2005). Next Day Load Curve Forecasting Using Hybrid Correction Method. IEEE Transactions on Power Systems, 20(1), 102-109. doi:10.1109/tpwrs.2004.831256 es_ES
dc.description.references Hsu, Y.-Y., & Yang, C.-C. (1991). Design of artificial neural networks for short-term load forecasting. Part 2: Multilayer feedforward networks for peak load and valley load forecasting. IEE Proceedings C Generation, Transmission and Distribution, 138(5), 414. doi:10.1049/ip-c.1991.0052 es_ES
dc.description.references Rejc, M., & Pantos, M. (2011). Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach. IEEE Transactions on Power Systems, 26(3), 1511-1521. doi:10.1109/tpwrs.2010.2096829 es_ES
dc.description.references Wang, Y., Xia, Q., & Kang, C. (2011). Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting. IEEE Transactions on Power Systems, 26(2), 500-507. doi:10.1109/tpwrs.2010.2052638 es_ES
dc.description.references Kebriaei, H., Araabi, B. N., & Rahimi-Kian, A. (2011). Short-Term Load Forecasting With a New Nonsymmetric Penalty Function. IEEE Transactions on Power Systems, 26(4), 1817-1825. doi:10.1109/tpwrs.2011.2142330 es_ES
dc.description.references Bishop, C. M. (1994). Neural networks and their applications. Review of Scientific Instruments, 65(6), 1803-1832. doi:10.1063/1.1144830 es_ES
dc.description.references Taylor, J. W., & Buizza, R. (2002). Neural network load forecasting with weather ensemble predictions. IEEE Transactions on Power Systems, 17(3), 626-632. doi:10.1109/tpwrs.2002.800906 es_ES
dc.description.references Chu, W.-C., Chen, Y.-P., Xu, Z.-W., & Lee, W.-J. (2011). Multiregion Short-Term Load Forecasting in Consideration of HI and Load/Weather Diversity. IEEE Transactions on Industry Applications, 47(1), 232-237. doi:10.1109/tia.2010.2090440 es_ES
dc.description.references Zhang, W. Y., Hong, W.-C., Dong, Y., Tsai, G., Sung, J.-T., & Fan, G. (2012). Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy, 45(1), 850-858. doi:10.1016/j.energy.2012.07.006 es_ES
dc.description.references Hong, W.-C. (2011). Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy, 36(9), 5568-5578. doi:10.1016/j.energy.2011.07.015 es_ES
dc.description.references Nose-Filho, K., Lotufo, A. D. P., & Minussi, C. R. (2011). Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network. IEEE Transactions on Power Delivery, 26(4), 2862-2869. doi:10.1109/tpwrd.2011.2166566 es_ES
dc.description.references Hernández, L., Baladrón, C., Aguiar, J., Carro, B., & Sánchez-Esguevillas, A. (2012). Classification and Clustering of Electricity Demand Patterns in Industrial Parks. Energies, 5(12), 5215-5228. doi:10.3390/en5125215 es_ES
dc.description.references Good, R. P., Kost, D., & Cherry, G. A. (2010). Introducing a Unified PCA Algorithm for Model Size Reduction. IEEE Transactions on Semiconductor Manufacturing, 23(2), 201-209. doi:10.1109/tsm.2010.2041263 es_ES
dc.description.references Hernández, L., Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Gómez, J. (2012). A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework. Sensors, 12(9), 11571-11591. doi:10.3390/s120911571 es_ES
dc.description.references Razavi, S., & Tolson, B. A. (2011). A New Formulation for Feedforward Neural Networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598. doi:10.1109/tnn.2011.2163169 es_ES
dc.description.references http://www.ree.es/sistema_electrico/pdf/indel/Atlas_INDEL_REE.pdf es_ES
dc.description.references Hsu, C.-C., & Chen, C.-Y. (2003). Regional load forecasting in Taiwan––applications of artificial neural networks. Energy Conversion and Management, 44(12), 1941-1949. doi:10.1016/s0196-8904(02)00225-x es_ES


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