Electricity load and price forecasting with influential factors in a deregulated power industry
With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response...
| Main Authors: | Hassan, S., Khosravi, A., Jaafar, J., Raza, M.Q. |
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| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.32026 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2014
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908614947&doi=10.1109%2fSYSOSE.2014.6892467&partnerID=40&md5=0a6e161e4834d9fa9ec25c37f78b36ac http://eprints.utp.edu.my/32026/ |
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| Summary: |
With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting. © 2014 IEEE. |
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