Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada
Electricity is an important asset that influences not only the economy, but political or social security of a country. Reliable and accurate planning and prediction of electricity demand for a country are therefore vital. In this paper, electricity demand in Ontario province of Canada from the year...
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| Veröffentlicht in: | Energy (Oxford) 2013-01, Vol.49 (1), p.323-328 |
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| creator | Zahedi, Gholamreza Azizi, Saeed Bahadori, Alireza Elkamel, Ali Wan Alwi, Sharifah R. |
| description | Electricity is an important asset that influences not only the economy, but political or social security of a country. Reliable and accurate planning and prediction of electricity demand for a country are therefore vital. In this paper, electricity demand in Ontario province of Canada from the year 1976–2005 is modeled by using an (adaptive neuro fuzzy inference system) ANFIS. A neuro fuzzy structure can be defined as an ANN (artificial neural network) which is trained by experimental data to find the parameters of (fuzzy inference system) FIS. Inputs for the model include number of employment, (gross domestic product) GDP, population, dwelling count and two meteorological parameters related to annual weather temperature. The data were collected and screened using statistical methods. Then, based on the data, a neuro-fuzzy model for the electricity demand is built. It was found that electricity demand is most sensitive to employment.
► Energy demand can be forecasted via artificial intelligent techniques. ► Employment is the most affecting parameter on energy demand. ► The neuro fuzzy model is capable of extrapolating the data very well. |
| doi_str_mv | 10.1016/j.energy.2012.10.019 |
| format | Article |
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► Energy demand can be forecasted via artificial intelligent techniques. ► Employment is the most affecting parameter on energy demand. ► The neuro fuzzy model is capable of extrapolating the data very well.</description><subject>Applied sciences</subject><subject>case studies</subject><subject>Demand</subject><subject>Electricity</subject><subject>Electricity demand</subject><subject>employment</subject><subject>Energy</subject><subject>Energy. Thermal use of fuels</subject><subject>Exact sciences and technology</subject><subject>Forecasting</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>gross domestic product</subject><subject>Marketing</subject><subject>Mathematical models</subject><subject>meteorological parameters</subject><subject>Neural networks</subject><subject>Neuro-fuzzy</subject><subject>planning</subject><subject>politics</subject><subject>prediction</subject><subject>statistical analysis</subject><subject>temperature</subject><issn>0360-5442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp9kc1u1DAQgHMAqaXlDSrhCxKXbD1xYhIOSNWq_EiVeoCerak9Xrxk7cV2FqWnvgNv2CfBq1QcOdkeffP3uaougK-Ag7zcrshT3MyrhkNTQisOw4vqlAvJ665tm5PqVUpbznnXD8NpdbgeSefotMszM7RDbxil7HaYXfBsSs5vGHqGBvfZHYh5mmKo7fTwMJd7_h3izw_simlMxFKezMxsDDuWfxC79RmjC2wfw8F5Tezp8Q9boy-1zquXFsdEr5_Ps-ru0_X39Zf65vbz1_XVTa3FIHJ9b4QdrNWdaNF2oqP-XmsABN0L7MsbG3hvZCsIOPKhK2txM6AF0_VGci7OqndL3TLDr6kspnYuaRpH9BSmpKDphZTdMPQFbRdUx5BSJKv2sWiIswKujmrVVi1q1VHtMVrUlrS3zx0waRxtRK9d-pfbyB4A5JF7s3AWg8JNLMzdt1KoTMll2wIU4uNCUBFycBRV0o6KN-Ni-SNlgvv_KH8BXLmevA</recordid><startdate>20130101</startdate><enddate>20130101</enddate><creator>Zahedi, Gholamreza</creator><creator>Azizi, Saeed</creator><creator>Bahadori, Alireza</creator><creator>Elkamel, Ali</creator><creator>Wan Alwi, Sharifah R.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20130101</creationdate><title>Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada</title><author>Zahedi, Gholamreza ; Azizi, Saeed ; Bahadori, Alireza ; Elkamel, Ali ; Wan Alwi, Sharifah R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-bd3f9ffc534af535e8bcc11a1c83a835ea217d643e10a0950000d9af1d58d6003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Applied sciences</topic><topic>case studies</topic><topic>Demand</topic><topic>Electricity</topic><topic>Electricity demand</topic><topic>employment</topic><topic>Energy</topic><topic>Energy. Thermal use of fuels</topic><topic>Exact sciences and technology</topic><topic>Forecasting</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy systems</topic><topic>gross domestic product</topic><topic>Marketing</topic><topic>Mathematical models</topic><topic>meteorological parameters</topic><topic>Neural networks</topic><topic>Neuro-fuzzy</topic><topic>planning</topic><topic>politics</topic><topic>prediction</topic><topic>statistical analysis</topic><topic>temperature</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zahedi, Gholamreza</creatorcontrib><creatorcontrib>Azizi, Saeed</creatorcontrib><creatorcontrib>Bahadori, Alireza</creatorcontrib><creatorcontrib>Elkamel, Ali</creatorcontrib><creatorcontrib>Wan Alwi, Sharifah R.</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zahedi, Gholamreza</au><au>Azizi, Saeed</au><au>Bahadori, Alireza</au><au>Elkamel, Ali</au><au>Wan Alwi, Sharifah R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada</atitle><jtitle>Energy (Oxford)</jtitle><date>2013-01-01</date><risdate>2013</risdate><volume>49</volume><issue>1</issue><spage>323</spage><epage>328</epage><pages>323-328</pages><issn>0360-5442</issn><coden>ENEYDS</coden><abstract>Electricity is an important asset that influences not only the economy, but political or social security of a country. Reliable and accurate planning and prediction of electricity demand for a country are therefore vital. In this paper, electricity demand in Ontario province of Canada from the year 1976–2005 is modeled by using an (adaptive neuro fuzzy inference system) ANFIS. A neuro fuzzy structure can be defined as an ANN (artificial neural network) which is trained by experimental data to find the parameters of (fuzzy inference system) FIS. Inputs for the model include number of employment, (gross domestic product) GDP, population, dwelling count and two meteorological parameters related to annual weather temperature. The data were collected and screened using statistical methods. Then, based on the data, a neuro-fuzzy model for the electricity demand is built. It was found that electricity demand is most sensitive to employment.
► Energy demand can be forecasted via artificial intelligent techniques. ► Employment is the most affecting parameter on energy demand. ► The neuro fuzzy model is capable of extrapolating the data very well.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2012.10.019</doi><tpages>6</tpages></addata></record> |
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| ispartof | Energy (Oxford), 2013-01, Vol.49 (1), p.323-328 |
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| language | eng |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Applied sciences case studies Demand Electricity Electricity demand employment Energy Energy. Thermal use of fuels Exact sciences and technology Forecasting Fuzzy Fuzzy logic Fuzzy systems gross domestic product Marketing Mathematical models meteorological parameters Neural networks Neuro-fuzzy planning politics prediction statistical analysis temperature |
| title | Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada |
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