Abstract:
Electric load forecasting plays a critical role for the reliable and efficient operation of power grids. In this paper we propose a load forecasting model using parallel ...Show MoreMetadata
Abstract:
Electric load forecasting plays a critical role for the reliable and efficient operation of power grids. In this paper we propose a load forecasting model using parallel radial basis function neural networks (RBFNN). The proposed implementation of RBFNN allows parallel computation therefore expedites the convergence of training process. The proposed model also employs a new hybrid chaotic genetic algorithm which introduces small scale chaotic variations into the best fit individuals in each iteration to locate an optimal set of parameters in RBFNN. We experiment the proposed load forecasting model with realistic demand data collected from both micro-grid as well as bulk grid levels, i.e., a local institutional micro-grid and one utility in the UK national grid. It is found that both cases can achieve acceptable forecasting accuracy with average error rate smaller than 4%, while forecasting the micro-grid load is more challenging than that of the bulk grid load due to the intermittent fluctuations within the former.
Date of Conference: 03-05 December 2013
Date Added to IEEE Xplore: 13 February 2014
Electronic ISBN:978-1-4799-0248-4
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- IEEE Keywords
- Index Terms
- Neural Network ,
- Load Forecasting ,
- Electricity Load Forecasting ,
- Small Scale ,
- Average Error ,
- Parallelization ,
- Radial Function ,
- Radial Basis Function ,
- Forecast Accuracy ,
- Time Series ,
- Artificial Neural Network ,
- Hidden Layer ,
- Training Time ,
- Fitness Function ,
- Local Optimum ,
- Chromosomal Gene ,
- Load Data ,
- Neurons In The Hidden Layer ,
- Hidden Neurons ,
- Office Buildings ,
- Demand-side Management ,
- Local Optimization Problem ,
- National Holidays ,
- Local Optimal Solution
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Load Forecasting ,
- Electricity Load Forecasting ,
- Small Scale ,
- Average Error ,
- Parallelization ,
- Radial Function ,
- Radial Basis Function ,
- Forecast Accuracy ,
- Time Series ,
- Artificial Neural Network ,
- Hidden Layer ,
- Training Time ,
- Fitness Function ,
- Local Optimum ,
- Chromosomal Gene ,
- Load Data ,
- Neurons In The Hidden Layer ,
- Hidden Neurons ,
- Office Buildings ,
- Demand-side Management ,
- Local Optimization Problem ,
- National Holidays ,
- Local Optimal Solution
- Author Keywords