A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting

• Published in: Energy (Oxford) 2019-05, Vol.174, p.460-477
• Main Authors: Singh, Priyanka, Dwivedi, Pragya, Kant, Vibhor
• Format: Article
• Language: English
• Subjects: Adaptation; Artificial neural networks; Economic forecasting; Electricity consumption; Electricity load forecasting; Environmental adaptation method; Evolutionary algorithms; Forecasting; Gaussian mutation; Genetic algorithms; Intelligence; Methodology; Methods; Neural network; Neural networks; Parameters
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.02.141

Load forecasting is a challenging task in power markets that require attention in generating accurate and stable load to deal with planning and management strategies. In past few years, several intelligence-based models have been introduced for precise load forecast. Among them, artificial neural network (ANN) seems more effective and capable to handle the non-linear behavior of load and generates an accurate forecast. However, it suffers from overfitting problem thus reducing the accuracy of load forecasts. To overcome this problem, a hybrid methodology namely ANN-IEAMCGM-R, for short-term load forecast is proposed in this paper. ANN is integrated with an enhanced evolutionary algorithm (IEAMCGM-R) to find optimal network weights. This evolutionary algorithm is composed of improved environmental adaptation method with real parameters (IEAM-R) and our proposed Controlled Gaussian Mutation (CGM) method to bring greater diversity within the population resulting in a higher convergence of solutions. The electric load data from the New England Power Pool (NEPOOL, ISO New England) and Australian Energy Market Operator (New South Wales (NSW), Australia) have been used to illustrate the efficacy of the proposed hybrid methodology. Results show that the proposed hybrid methodology generates higher accuracy than other state-of-the-art algorithms. •Proposal of Controlled Gaussian Mutation for enhancing the diversity of IEAM-R.•Proposal of enhanced optimization algorithm, namely IEAMCGM-R.•A hybrid methodology is presented for load forecasting.•Experimental results show that ANN-IEAMCGM-R generates least forecasting error.•Statistical test results ensures the statistical signi_cance of forecasted load.