Forecasting of Short-Term Electric Load Using Application of Wavelets with Feed-Forward Neural Networks
-
N.M. Pindoriya
An accurate and efficient Short-Term Load Forecasting (STLF) plays a vital role for economic operational planning of both regulated and restructured power systems. This paper presents the STLF models by introducing wavelet transforms, in different ways, with feed-forward Neural Networks (NNs). First, a wavelet-based NN is modeled, where the forecasting has been accomplished in three-stages and the wavelet technique is employed to decompose/reconstruct the original signals and non-linearity of the decomposed signals. Second, an Adaptive Wavelet Neural Network (AWNN) is modeled, which is a new class of NN with continuous wavelet function as the hidden layer node's activation function. Unlike the first model, AWNN does not externally decompose/ reconstruct the original signals and, therefore, this model deals with the problem related to loss of high frequency information that might occur in the wavelet-based NN model. AWNN continuously updates the wavelet parameters (translation and dilation) and layer weights through a back-propagation training algorithm as in classical NNs. The performances of these two models are compared with Multi-Layer Perceptron NN (MLPNN) with the application of day-ahead and hour-ahead load forecasting in the California electricity market. The results are also compared with California Independent System Operator (CAISO)'s forecasted system loads. It is found that due to faster and accurate training capability, AWNN outperforms the MLPNN, wavelet-based NN and CAISO load forecasts.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Articles in the same Issue
- Article
- Design of New Load Balancers for Modern High Speed Railway Systems
- Alleviation of Network Overloads Using Concept of Virtual Flows
- Forecasting of Short-Term Electric Load Using Application of Wavelets with Feed-Forward Neural Networks
- Efficient Market Design and Public Goods, Part I: Economic Models
- Efficient Market Design and Public Goods, Part II: Theoretical Results
- Classification of Power Quality Disturbances Using Parzen Kernels
- Detection of Saturation and Reconstruction of the Secondary Current of a CT
- A New Blocking Scheme for Distance Protection during Power Swings
- Unit Commitment by Structure Based Solution and Efficient Lagrangian Relaxation
- Modeling Shaft Damping of Turbine-Generator in Electromagnetic Transient Simulation
Articles in the same Issue
- Article
- Design of New Load Balancers for Modern High Speed Railway Systems
- Alleviation of Network Overloads Using Concept of Virtual Flows
- Forecasting of Short-Term Electric Load Using Application of Wavelets with Feed-Forward Neural Networks
- Efficient Market Design and Public Goods, Part I: Economic Models
- Efficient Market Design and Public Goods, Part II: Theoretical Results
- Classification of Power Quality Disturbances Using Parzen Kernels
- Detection of Saturation and Reconstruction of the Secondary Current of a CT
- A New Blocking Scheme for Distance Protection during Power Swings
- Unit Commitment by Structure Based Solution and Efficient Lagrangian Relaxation
- Modeling Shaft Damping of Turbine-Generator in Electromagnetic Transient Simulation
