Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting

Short-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more "regularity" to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology. (C) 2019 Elsevier B.V. All rights reserved.