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A platform for research: civil engineering, architecture and urbanism
Day Ahead Regional Electrical Load Forecasting Using ANFIS Techniques
https://orcid.org/http://orcid.org/0000-0001-8215-7551
Short-term load forecasting is a powerful tool for improvement of operation, energy efficiency and reliability of power systems. Researchers are continuously working to improve outcomes of short-term load forecasting (STLF). In this paper, three different ANFIS models are developed for STLF. The proposed models are tested for prediction of load demand of Rajasthan region of India, from fifteen minutes to one week ahead for particular time of the day of year 2015. Rajasthan region has a typical load curve as it has a land area of 342,239 km2 and population of 68 million, with acute climatic conditions. The outcomes obtained from proposed models are compared with outcomes of significant strategies available in literature based on ANN. This comparison reveals that the proposed RR (Rajasthan Region) model is a competitive technique among all other strategies. The results are compared on the basis of MAE, APE and MAPE for fifteen forecasting samples.
Day Ahead Regional Electrical Load Forecasting Using ANFIS Techniques
https://orcid.org/http://orcid.org/0000-0001-8215-7551
Short-term load forecasting is a powerful tool for improvement of operation, energy efficiency and reliability of power systems. Researchers are continuously working to improve outcomes of short-term load forecasting (STLF). In this paper, three different ANFIS models are developed for STLF. The proposed models are tested for prediction of load demand of Rajasthan region of India, from fifteen minutes to one week ahead for particular time of the day of year 2015. Rajasthan region has a typical load curve as it has a land area of 342,239 km2 and population of 68 million, with acute climatic conditions. The outcomes obtained from proposed models are compared with outcomes of significant strategies available in literature based on ANN. This comparison reveals that the proposed RR (Rajasthan Region) model is a competitive technique among all other strategies. The results are compared on the basis of MAE, APE and MAPE for fifteen forecasting samples.
Day Ahead Regional Electrical Load Forecasting Using ANFIS Techniques
https://orcid.org/http://orcid.org/0000-0001-8215-7551
Short-term load forecasting is a powerful tool for improvement of operation, energy efficiency and reliability of power systems. Researchers are continuously working to improve outcomes of short-term load forecasting (STLF). In this paper, three different ANFIS models are developed for STLF. The proposed models are tested for prediction of load demand of Rajasthan region of India, from fifteen minutes to one week ahead for particular time of the day of year 2015. Rajasthan region has a typical load curve as it has a land area of 342,239 km2 and population of 68 million, with acute climatic conditions. The outcomes obtained from proposed models are compared with outcomes of significant strategies available in literature based on ANN. This comparison reveals that the proposed RR (Rajasthan Region) model is a competitive technique among all other strategies. The results are compared on the basis of MAE, APE and MAPE for fifteen forecasting samples.
Day Ahead Regional Electrical Load Forecasting Using ANFIS Techniques
J. Inst. Eng. India Ser. B
Rathor, Ram Dayal (author) / Bharagava, Annapurna (author)
Journal of The Institution of Engineers (India): Series B ; 101 ; 475-495
2020-10-01
21 pages
Article (Journal)
Electronic Resource
English
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