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In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.", "publicationTitle": "IEEE Transactions on Sustainable Energy", "publisher": "", "place": "", "date": "July 2014", "volume": "5", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "917-926", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TSTE.2014.2313600", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1949-3029", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2014-07-07T22:46:54Z", "dateModified": "2014-07-07T22:46:54Z" } }, { "key": "S2T7EMPH", "version": 89, "library": { "type": "group", "id": 267029, "name": "EPIC-Forecast-Testbed", "links": { "alternate": { "href": "https://www.zotero.org/groups/epic-forecast-testbed", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267029/items/S2T7EMPH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epic-forecast-testbed/items/S2T7EMPH", "type": "text/html" } }, "meta": { "createdByUser": { "id": 131891, "username": "amills61", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/amills61", "type": "text/html" } } }, "creatorSummary": "Wang et al.", "parsedDate": "2008", "numChildren": 1 }, "data": { "key": "S2T7EMPH", "version": 89, "itemType": "journalArticle", "title": "Security-Constrained Unit Commitment With Volatile Wind Power Generation", "creators": [ { "creatorType": "author", "firstName": "Jianhui", "lastName": "Wang" }, { "creatorType": "author", "firstName": "M.", "lastName": "Shahidehpour" }, { "creatorType": "author", "firstName": "Zuyi", "lastName": "Li" } ], "abstractNote": "This paper presents a security-constrained unit commitment (SCUC) algorithm which takes into account the intermittency and volatility of wind power generation. The UC problem is solved in the master problem with the forecasted intermittent wind power generation. Next, possible scenarios are simulated for representing the wind power volatility. The initial dispatch is checked in the subproblem and generation redispatch is considered for satisfying the hourly volatility of wind power in simulated scenarios. If the redispatch fails to mitigate violations, Benders cuts are created and added to the master problem to revise the commitment solution. The iterative process between the commitment problem and the feasibility check subproblem will continue until simulated wind power scenarios can be accommodated by redispatch. Numerical simulations indicate the effectiveness of the proposed SCUC algorithm for managing the security of power system operation by taking into account the intermittency and volatility of wind power generation.", "publicationTitle": "Power Systems, IEEE Transactions on", "publisher": "", "place": "", "date": "2008", "volume": "23", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "1319-1327", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Power Systems, IEEE Transactions on", "DOI": "10.1109/TPWRS.2008.926719", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0885-8950", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2014-05-28T22:30:43Z", "dateModified": "2014-07-07T19:15:46Z" } }, { "key": "UI7DJS34", "version": 86, "library": { "type": "group", "id": 267029, "name": "EPIC-Forecast-Testbed", "links": { "alternate": { "href": "https://www.zotero.org/groups/epic-forecast-testbed", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267029/items/UI7DJS34", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epic-forecast-testbed/items/UI7DJS34", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267029/items/359TZS8H", "type": "application/json" } }, "meta": { "createdByUser": { "id": 131891, "username": "amills61", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/amills61", "type": "text/html" } } } }, "data": { "key": "UI7DJS34", "version": 86, "parentItem": "359TZS8H", "itemType": "attachment", "linkMode": "imported_url", "title": "IEEE Xplore Abstract Record", "accessDate": "2014-07-07T16:57:57Z", "url": "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6570751&tag=1", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "abs_all.html", "md5": "f1975b7fbbd73ef5b43658abe83d1f43", "mtime": 1404752277000, "tags": [], "relations": {}, "dateAdded": "2014-07-07T16:57:57Z", "dateModified": "2014-07-07T16:57:57Z" } }, { "key": "359TZS8H", "version": 85, "library": { "type": "group", "id": 267029, "name": "EPIC-Forecast-Testbed", "links": { "alternate": { "href": "https://www.zotero.org/groups/epic-forecast-testbed", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267029/items/359TZS8H", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epic-forecast-testbed/items/359TZS8H", "type": "text/html" } }, "meta": { "createdByUser": { "id": 131891, "username": "amills61", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/amills61", "type": "text/html" } } }, "creatorSummary": "Vrakopoulou et al.", "parsedDate": "2013-11", "numChildren": 2 }, "data": { "key": "359TZS8H", "version": 85, "itemType": "journalArticle", "title": "A Probabilistic Framework for Reserve Scheduling and Security Assessment of Systems With High Wind Power Penetration", "creators": [ { "creatorType": "author", "firstName": "M.", "lastName": "Vrakopoulou" }, { "creatorType": "author", "firstName": "K.", "lastName": "Margellos" }, { "creatorType": "author", "firstName": "J.", "lastName": "Lygeros" }, { "creatorType": "author", "firstName": "G.", "lastName": "Andersson" } ], "abstractNote": "We propose a probabilistic framework to design an N-1 secure day-ahead dispatch and determine the minimum cost reserves for power systems with wind power generation. We also identify a reserve strategy according to which we deploy the reserves in real-time operation, which serves as a corrective control action. To achieve this, we formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines and the generation limits. To incorporate a reserve decision scheme, we take into account the steady-state behavior of the secondary frequency controller and, hence, consider the deployed reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program. To achieve tractability, we propose a convex reformulation and a heuristic algorithm, whereas to deal with the chance constraint we use a scenario-based-approach and an approach that considers only the quantiles of the stationary distribution of the wind power error. To quantify the effectiveness of the proposed methodologies and compare them in terms of cost and performance, we use the IEEE 30-bus network and carry out Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain-based model.", "publicationTitle": "IEEE Transactions on Power Systems", "publisher": "", "place": "", "date": "November 2013", "volume": "28", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "3885-3896", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TPWRS.2013.2272546", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0885-8950", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2014-07-07T16:57:55Z", "dateModified": "2014-07-07T16:57:55Z" } }, { "key": "UGQJQU47", "version": 84, "library": { "type": "group", "id": 267029, "name": "EPIC-Forecast-Testbed", "links": { "alternate": { "href": "https://www.zotero.org/groups/epic-forecast-testbed", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267029/items/UGQJQU47", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epic-forecast-testbed/items/UGQJQU47", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267029/items/6CW33FWQ", "type": "application/json" } }, "meta": { "createdByUser": { "id": 131891, "username": "amills61", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/amills61", "type": "text/html" } } } }, "data": { "key": "UGQJQU47", "version": 84, "parentItem": "6CW33FWQ", "itemType": "attachment", "linkMode": "linked_url", "title": "Google Books Link", "accessDate": "2014-07-07T01:55:28Z", "url": "http://books.google.com/books?id=IHUYAwAAQBAJ", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2014-07-07T01:55:28Z", "dateModified": "2014-07-07T01:55:28Z" } } ]