Weather sensitive short-term load forecasting using non-fully connected artificial neural networks
Artificial Neural Networks (ANN) are parallel distributed models which are capable of performing nonlinear modeling and adaptation without making functional assumptions. This thesis presents a non-fully connected ANN model with cascaded training strategy for forecasting weather sensitive electric loads. The proposed model is capable of forecasting the hourly load for an entire week and has been implemented on real load data. The average absolute forecast errors of the one hour ahead forecast in the Winter and Summer test cases are shown to be 3.42% and 3.19% respectively; the average absolute forecast errors at peak in both test cases are shown to be 1.19% and 3.45%. The model is superior to the conventional statistical method in model life cycle length, degree of recursiveness and accuracy. With non-fully connected network structure, the network training time is reduced and the degree of robustness is enhanced. By applying the cascaded learning strategy, the network performance is improved. General guidelines in constructing ANN forecasting models are developed. Intensive illustrations are also provided to explain the fundamental behavior of the ANN models in time series applications.
- Research Organization:
- Wisconsin Univ., Milwaukee, WI (United States)
- OSTI ID:
- 7167213
- Resource Relation:
- Other Information: Thesis (Ph.D.)
- Country of Publication:
- United States
- Language:
- English
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