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As wind speed is characterized by high autocorrelation and inherent volatility, it is difficult to predict with a single model. The aim of this study is to develop a new hybrid model for predicting the short wind speed at 1 h intervals up to 5 h based on wavelet packet decomposition, crisscross optimization algorithm and artificial neural networks. In the data pre-processing phase, the wavelet packet technique is used to decompose the original wind speed series into subseries. For each transformed components with different frequency sub-bands, the back-propagation neural network optimized by crisscross optimization algorithm is employed to predict the multi-step ahead wind speed. The eventual predicted results are obtained through aggregate calculation. To validate the effectiveness of the proposed approach, two wind speed series collected from a wind observation station located in the Netherlands are used to do the multi-step wind speed forecasting. To reduce the statistical errors, all forecasting methods are executed 50 times independently. The results of this study show that: (1) the proposed crisscross optimization algorithm has significant advantage over the back-propagation algorithm and particle swarm optimization in addressing the prematurity problems when applied to train the neural network. 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The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or meteorological models, gives significantly lower prediction error than the elementary persistent approach. This was combined with the characteristics of the wind speed data, which are determined by the power spectrum values, distinguished by the spectral gap in intervals between 20 minutes and 2 hours. The finally proposed methodology is based on the multi-step forecasting of 10 minutes averaged data and the subsequent averaging to generate mean hourly predictions. When applied to two independent data sets, this approach outperformed by a factor of four, the conventional one which utilizes past mean hourly wind speed values as inputs to the forecasting models.", "publicationTitle": "Renewable Energy", "publisher": "", "place": "", "date": "octobre 2002", "volume": "27", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "163-174", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Renewable Energy", "DOI": "10.1016/S0960-1481(01)00193-8", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0960148101001938", "accessDate": "2013-05-24T11:38:28Z", "PMID": "", "PMCID": "", "ISSN": "0960-1481", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Forecasting", "type": 1 }, { "tag": "Mean hourly wind speed", "type": 1 }, { "tag": "Neural networks", "type": 1 }, { "tag": "Time series", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2013-05-24T11:38:28Z", "dateModified": "2013-05-24T11:38:28Z" } }, { "key": "2W8FU5SR", "version": 20, "library": { "type": "group", "id": 147098, "name": "Forcasting Wind Speeds", "links": { "alternate": { "href": "https://www.zotero.org/groups/forcasting_wind_speeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/147098/items/2W8FU5SR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/forcasting_wind_speeds/items/2W8FU5SR", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/147098/items/NQT5FQF9", "type": "application/json" } }, "meta": { "createdByUser": { "id": 241213, "username": "cpaoli", "name": "Christophe PAOLI", "links": { "alternate": { "href": "https://www.zotero.org/cpaoli", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "2W8FU5SR", "version": 20, "parentItem": "NQT5FQF9", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Full Text PDF", "accessDate": "2013-05-24T11:34:34Z", "url": "http://www.sciencedirect.com.gate6.inist.fr/science?_ob=MiamiImageURL&_cid=271431&_user=4013381&_pii=S0960148199001251&_check=y&_origin=article&_zone=toolbar&_coverDate=01-Sep-2000&view=c&originContentFamily=serial&wchp=dGLzVlk-zSkzk&md5=d7120d78e34da1842544e254272bbc6f&pid=1-s2.0-S0960148199001251-main.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Sfetsos - 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In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Neural Logic Networks. 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"Sfetsos" }, { "creatorType": "author", "firstName": "A. H.", "lastName": "Coonick" } ], "abstractNote": "This paper introduces a new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface. In addition to the traditional linear methods, several artificial-intelligence-based techniques are studied. These include linear, feed-forward, recurrent Elman and Radial Basis neural networks alongside the adaptive neuro-fuzzy inference scheme. The problem is examined initially for the univariate case, and is extended to include additional meteorological parameters in the process of estimating the optimum model. The results indicate that the developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. 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Wind data for ten consecutive years (1991-2000) are available for\nthis area. The network was trained to predict the mean monthly hourly wind speed of a year\n(e.g. 1994) by using the values of wind speed for the same month, same hour for the three\nprevious years (e.g. 1991-1993), consecutively. The data for the wind speed up to the year 1999\nhave been used for the training of the network whereas those for the years 1997-1999 (input)\nand 2000 (output) were used for the validation of the network. It should be noted that the data\nfor the year 2000 were completely unknown to the network. The wind speed for the validation\ndata set was predicted with a correlation coefficient of 0.82 which is satisfactory for wind speed\nwhich is very unstable. 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