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Fuzzy Inference Model for Short-Term Load Forecasting
https://orcid.org/http://orcid.org/0000-0003-3643-4524
For planning and operation of an energy management system, load forecasting (LF) is essential. For smooth power system operation (PS), LF enhances the energy-efficient and reliable operation. LF also helps to calculate energy supplied by utilities to meet the load plus the energy lost in the PS. Every day, it is necessary to schedule the power generation for the next day. So, short-term load forecasting (STLF) is used to calculate the power dispatch for the next day. In unit commitment, economic allocation of generation and maintenance schedules, STLF is also used. So, to make the STLF more effective, fuzzy logic (FL) is used here. FL is essential for weather-sensitive and historical load data for forecasting the load. The fuzzy decision rule identifies the nonlinear relationship between the input and output data. The historical load and hourly data like temperature, humidity (relative humidity) and wind speed are used for input data. For the training and testing, the hourly based load data are collected from the state load dispatch and communication center of Rajasthan Vidyut Prasaran Nigam, Jaipur (JVN). The triangular membership function of the fuzzy logic model is used to predict the load. The performance of the work is determined by the mean absolute percentage error (MAPE) and the MAPE value for pre-holiday (Saturday), holiday (Sunday), post-holiday, and working day is 0.37%, 0.24%, 0.09%, and 0.09%, respectively.
Fuzzy Inference Model for Short-Term Load Forecasting
https://orcid.org/http://orcid.org/0000-0003-3643-4524
For planning and operation of an energy management system, load forecasting (LF) is essential. For smooth power system operation (PS), LF enhances the energy-efficient and reliable operation. LF also helps to calculate energy supplied by utilities to meet the load plus the energy lost in the PS. Every day, it is necessary to schedule the power generation for the next day. So, short-term load forecasting (STLF) is used to calculate the power dispatch for the next day. In unit commitment, economic allocation of generation and maintenance schedules, STLF is also used. So, to make the STLF more effective, fuzzy logic (FL) is used here. FL is essential for weather-sensitive and historical load data for forecasting the load. The fuzzy decision rule identifies the nonlinear relationship between the input and output data. The historical load and hourly data like temperature, humidity (relative humidity) and wind speed are used for input data. For the training and testing, the hourly based load data are collected from the state load dispatch and communication center of Rajasthan Vidyut Prasaran Nigam, Jaipur (JVN). The triangular membership function of the fuzzy logic model is used to predict the load. The performance of the work is determined by the mean absolute percentage error (MAPE) and the MAPE value for pre-holiday (Saturday), holiday (Sunday), post-holiday, and working day is 0.37%, 0.24%, 0.09%, and 0.09%, respectively.
Fuzzy Inference Model for Short-Term Load Forecasting
https://orcid.org/http://orcid.org/0000-0003-3643-4524
For planning and operation of an energy management system, load forecasting (LF) is essential. For smooth power system operation (PS), LF enhances the energy-efficient and reliable operation. LF also helps to calculate energy supplied by utilities to meet the load plus the energy lost in the PS. Every day, it is necessary to schedule the power generation for the next day. So, short-term load forecasting (STLF) is used to calculate the power dispatch for the next day. In unit commitment, economic allocation of generation and maintenance schedules, STLF is also used. So, to make the STLF more effective, fuzzy logic (FL) is used here. FL is essential for weather-sensitive and historical load data for forecasting the load. The fuzzy decision rule identifies the nonlinear relationship between the input and output data. The historical load and hourly data like temperature, humidity (relative humidity) and wind speed are used for input data. For the training and testing, the hourly based load data are collected from the state load dispatch and communication center of Rajasthan Vidyut Prasaran Nigam, Jaipur (JVN). The triangular membership function of the fuzzy logic model is used to predict the load. The performance of the work is determined by the mean absolute percentage error (MAPE) and the MAPE value for pre-holiday (Saturday), holiday (Sunday), post-holiday, and working day is 0.37%, 0.24%, 0.09%, and 0.09%, respectively.
Fuzzy Inference Model for Short-Term Load Forecasting
J. Inst. Eng. India Ser. B
Panda, Saroj Kumar (author) / Ray, Papia (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 1939-1948
2022-12-01
10 pages
Article (Journal)
Electronic Resource
English
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