{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T11:43:44Z","timestamp":1768736624344,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,10]],"date-time":"2017-11-10T00:00:00Z","timestamp":1510272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sustainability"],"abstract":"<jats:p>Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.<\/jats:p>","DOI":"10.3390\/su9112065","type":"journal-article","created":{"date-parts":[[2017,11,10]],"date-time":"2017-11-10T11:12:26Z","timestamp":1510312346000},"page":"2065","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators"],"prefix":"10.3390","volume":"9","author":[{"given":"Saber","family":"Talari","sequence":"first","affiliation":[{"name":"C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-5355","authenticated-orcid":false,"given":"Miadreza","family":"Shafie-khah","sequence":"additional","affiliation":[{"name":"C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8328-9708","authenticated-orcid":false,"given":"Gerardo","family":"Os\u00f3rio","sequence":"additional","affiliation":[{"name":"C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7332-9726","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China"},{"name":"Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}]},{"given":"Alireza","family":"Heidari","sequence":"additional","affiliation":[{"name":"Australian Energy Research Institute (AERI), School of Electrical Engineering and Telecommunications, The University of New South Wales (UNSW), Sydney, NSW 2052, Australia"}]},{"given":"Jo\u00e3o","family":"Catal\u00e3o","sequence":"additional","affiliation":[{"name":"C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilh\u00e3, Portugal"},{"name":"INESC TEC, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal"},{"name":"INESC-ID, Instituto Superior T\u00e9cnico, University of Lisbon, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shahidehpour, M., Yamin, H., and Li, Z. (2002). Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, Institute of Electrical and Electronics Engineers, Wiley-Interscience.","DOI":"10.1002\/047122412X"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0306-2619(03)00096-5","article-title":"Forecasting electricity spot-prices using linear univariate time-series models","volume":"77","author":"Hlouskova","year":"2004","journal-title":"Appl. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1109\/TPWRS.2002.804943","article-title":"ARIMA models to predict next-day electricity prices","volume":"18","author":"Contreras","year":"2003","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/TPWRS.2005.846044","article-title":"A GARCH forecasting model to predict day-ahead electricity prices","volume":"20","author":"Garcia","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/TPWRS.2004.840412","article-title":"Modeling and Forecasting Electricity Prices with Input\/Output Hidden Markov Models","volume":"20","author":"MateoGonzalez","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1016\/S0196-8904(01)00127-3","article-title":"Prediction of system marginal price of electricity using wavelet transform analysis","volume":"43","author":"Kim","year":"2002","journal-title":"Energy Convers. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1080\/07313560050129855","article-title":"Prediction of System Marginal Prices by Wavelet Transform and Neural Networks","volume":"28","author":"Yao","year":"2000","journal-title":"Electr. Mach. Power Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1016\/j.enconman.2010.10.047","article-title":"Price forecasting of day-ahead electricity markets using a hybrid forecast method","volume":"52","author":"Moghaddam","year":"2011","journal-title":"Energy Convers. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1109\/TPWRS.2005.846106","article-title":"A particle swarm optimization to identifying the ARMAX model for short-term load forecasting","volume":"20","author":"Huang","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TPWRS.2005.846054","article-title":"Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models","volume":"20","author":"Conejo","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3606","DOI":"10.1016\/j.apenergy.2010.05.012","article-title":"Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models","volume":"87","author":"Tan","year":"2010","journal-title":"Appl. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MPAE.2006.1597990","article-title":"Energy price forecasting-problems and proposals for such predictions","volume":"4","author":"Amjady","year":"2006","journal-title":"IEEE Power Energy Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1109\/TPWRS.2006.873409","article-title":"Day-Ahead Price Forecasting of Electricity Markets by a New Fuzzy Neural Network","volume":"21","author":"Amjady","year":"2006","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1016\/j.epsr.2006.09.022","article-title":"Short-term electricity prices forecasting in a competitive market: A neural network approach","volume":"77","author":"Mariano","year":"2007","journal-title":"Electr. Power Syst. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/S0925-2312(98)00079-4","article-title":"A neural network based estimator for electricity spot-pricing with particular reference to weekend and public holidays","volume":"23","author":"Wang","year":"1998","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1109\/59.780895","article-title":"Electricity price short-term forecasting using artificial neural networks","volume":"14","author":"Szkuta","year":"1999","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_17","unstructured":"Gao, F., Guan, X., Cao, X.-R., and Papalexopoulos, A. (2000, January 16\u201320). Forecasting power market clearing price and quantity using a neural network method. Proceedings of the 2000 Power Engineering Society Summer Meeting, Seattle, WA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1049\/ip-gtd:20020371","article-title":"Locational marginal price forecasting in deregulated electricity markets using artificial intelligence","volume":"149","author":"Hong","year":"2002","journal-title":"IEE Proc. Gener. Transm. Distrib."},{"key":"ref_19","unstructured":"Zhang, L., and Luh, P.B. (2002, January 27\u201331). Power market clearing price prediction and confidence interval estimation with fast neural network learning. Proceedings of the 2002 IEEE Power Engineering Society Winter Meeting, New York, NY, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/TPWRS.2003.821470","article-title":"Energy price forecasting in the Ontario competitive power system market","volume":"19","author":"Rodriguez","year":"2004","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/5.823996","article-title":"Forecasting loads and prices in competitive power markets","volume":"88","author":"Bunn","year":"2000","journal-title":"Proc. IEEE"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1002\/cplx.21713","article-title":"Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market","volume":"21","author":"Jalili","year":"2016","journal-title":"Complexity"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1002\/cplx.21668","article-title":"Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods","volume":"21","author":"Hosseini","year":"2016","journal-title":"Complexity"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1109\/TPWRS.2004.837759","article-title":"Improving market clearing price prediction by using a committee machine of neural networks","volume":"19","author":"Guo","year":"2004","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1002\/cplx.21792","article-title":"Day-ahead price forecasting based on hybrid prediction model","volume":"21","author":"Olamaee","year":"2016","journal-title":"Complexity"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/TPWRS.2004.840416","article-title":"Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method","volume":"20","author":"Zhang","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_27","unstructured":"Hong, Y.Y., and Hsiao, C.-Y. (February, January 28). Locational marginal price forecasting in deregulated electric markets using a recurrent neural network. Proceedings of the 2001 IEEE Power Engineering Society Winter Meeting, Columbus, OH, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, K., and Shi, Q. (2009, January 24\u201326). Power Futures Price Forecasting Based on RBF Neural Network. Proceedings of the 2009 International Conference on Business Intelligence and Financial Engineering, Beijing, China.","DOI":"10.1109\/BIFE.2009.21"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.ijforecast.2014.08.008","article-title":"Electricity price forecasting: A review of the state-of-the-art with a look into the future","volume":"30","author":"Weron","year":"2014","journal-title":"Int. J. Forecast."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ijepes.2008.09.003","article-title":"Electricity price forecasting in deregulated markets: A review and evaluation","volume":"31","author":"Aggarwal","year":"2009","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cadenas, E., Rivera, W., Campos-Amezcua, R., and Heard, C. (2016). Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model. Energies, 9.","DOI":"10.3390\/en9020109"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.chaos.2004.11.015","article-title":"Wavelet-based prediction of oil prices","volume":"25","author":"Yousefi","year":"2005","journal-title":"Chaos Solitons Fractals"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.resourpol.2007.06.002","article-title":"Wavelet- and SVM-based forecasts: An analysis of the U.S. metal and materials manufacturing industry","volume":"32","author":"Fernandez","year":"2007","journal-title":"Resour. Policy"},{"key":"ref_35","unstructured":"Inicio (2017, August 24). Ontology Mapping within an Interactive and Extensible Environment (OMIE). Available online: http:\/\/www.omie.es\/en\/inicio."},{"key":"ref_36","unstructured":"Windfinder.com (2017, August 25). Windfinder\u2014Wind, Wave & Weather Reports, Forecasts & Statistics Worldwide. Available online: https:\/\/www.windfinder.com."}],"container-title":["Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2071-1050\/9\/11\/2065\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:51Z","timestamp":1760208531000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2071-1050\/9\/11\/2065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,10]]},"references-count":36,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["su9112065"],"URL":"https:\/\/doi.org\/10.3390\/su9112065","relation":{},"ISSN":["2071-1050"],"issn-type":[{"value":"2071-1050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,10]]}}}