Weather sensitive short-term load forecasting using nonfully connected artificial neural network
Journal Article
·
· IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States)
- Univ. of Wisconsin-Milwaukee, Milwaukee, WI (US)
This paper presents an Artificial Neural Network (ANN) model for forecasting weather sensitive loads. The proposed model is capable of forecasting the hourly loads for an entire week. The model is not fully connected; hence, it has a shorter training time than the fully-connected ANN. The proposed model can differentiate between the weekday loads and the weekend loads. The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model. This ANN model has been implemented on real load data. The average percentage peak error for the test cases is 1.12%.
- OSTI ID:
- 7281216
- 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) Journal Issue: 3 Vol. 7:3; ISSN ITPSE; ISSN 0885-8950
- Country of Publication:
- United States
- Language:
- English
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