Short-term load forecasting with using multiple linear regression
| Main Authors: | Bhatti Dhaval, Anuradha Deshpande |
|---|---|
| Format: | Article |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4108272 |
| ctrlnum |
4108272 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Bhatti Dhaval</creator><creator>Anuradha Deshpande</creator><date>2020-08-01</date><description>In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.</description><identifier>https://zenodo.org/record/4108272</identifier><identifier>10.11591/ijece.v10i4.pp3911-3917</identifier><identifier>oai:zenodo.org:4108272</identifier><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>International Journal of Electrical and Computer Engineering (IJECE) 10(4) 3911-3917</source><subject>Multiple linear regression</subject><subject>Regression</subject><subject>Short term load forecasting</subject><title>Short-term load forecasting with using multiple linear regression</title><type>Journal:Article</type><type>Journal:Article</type><recordID>4108272</recordID></dc>
|
| format |
Journal:Article Journal |
| author |
Bhatti Dhaval Anuradha Deshpande |
| title |
Short-term load forecasting with using multiple linear regression |
| publishDate |
2020 |
| topic |
Multiple linear regression Regression Short term load forecasting |
| url |
https://zenodo.org/record/4108272 |
| contents |
In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained. |
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library:public library |
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| repoId |
IOS16997 |
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2022-06-06T05:01:11Z |
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