An Adaptive Evolutionary Neural Network Model for Load Management in Smart Grid Environment

To empower the management of smart meters' demand load within a smart grid environment, this paper presents a Feed-forward Neural Network with ADaptive Evolutionary Learning Approach (ADELA). In this model, the load forecasting information is propagated via neurons of input and multiple hidden layers and the final estimated output is achieved with the help of the sigmoid activation function. An improved evolutionary algorithm is proposed for training and adjusting the interconnecting weights among the layers of the intended neural network. This model is capable of addressing the critical challenges of high volatility, uncertainty, missing smart meters data, and sudden upsurge and plunge in electricity demand. The proposed algorithm is able to learn the best suitable evolutionary operators from a given pool of operators and the probabilities associated with them. The proposed load forecasting approach is simulated over three real-world smart meter datasets, including the Australian Smart Grid Smart City project, the Irish Commission for Energy Regulation, and UMass Smart. The performance evaluation and comparison of the proposed approach with the existing state-of-the-art approaches revealed a relative improvement of up to 46.93%, 5.05%, and 2.20% in forecast accuracy over the Smart Grid Smart City, UMass Smart and the Irish Commission for Energy Regulation datasets, respectively.