Neural network based short term load forecasting
- National Sun Yat-Sen Univ., Kahosiung (Taiwan, Province of China). Dept. of Electrical Engineering
- Harris Corp., Melbourne, FL (United States). Controls and Composition Div.
The artificial neural network (ANN) technique for short term load forecasting (STLF) has been proposed by several authors, and gained a lot of attention recently. In order to evaluate ANN as a viable technique for STLF, one has to evaluate the performance of ANN methodology for practical considerations of STLF problems. This paper makes an attempt to address these issues. The paper presents the results of a study to investigate whether the ANN model is system dependent, and/or case dependent. Data from two utilities were used in modeling and forecasting. In addition, the effectiveness of a next 24 hour ANN model is predicting 24 hour load profile at one time was compared with the traditional next one hour ANN model.
- OSTI ID:
- 5997437
- Journal Information:
- IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States) Vol. 8:1; ISSN ITPSEG; ISSN 0885-8950
- Country of Publication:
- United States
- Language:
- English
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292000* -- Energy Planning & Policy-- Supply
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ARTIFICIAL INTELLIGENCE
COMPUTERIZED SIMULATION
DEMAND
ENERGY SYSTEMS
FORECASTING
NEURAL NETWORKS
POWER DEMAND
POWER SYSTEMS
SIMULATION