Short-term load forecasting using neural networks
โ Scribed by S.J. Kiartzis; A.G. Bakirtzis; V. Petridis
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 503 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0378-7796
No coin nor oath required. For personal study only.
โฆ Synopsis
An artificial neural network (ANN) model for short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting the next 24-hour load profile at one time, as opposed to the usual 'next one hour' ANN models. The inputs to the ANN are load profiles of the two previous days and daily maximum and minimum temperature forecasts. The network is trained to learn the next day's load profile. Testing of the model with one year of data from the Greek interconnected power system resulted in a 2.66% average absolute forecast error.
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Short-term electric load forecasting is an important requirement for electric system operation. This paper employs a feed-forward neural network with a back-propagation algorithm for three types of short-term electric load forecasting: daily peak (valley) load, hourly load and the total load. The fo