Big Data Analytics for Demand Response in Smart Grid

Abstract

The power industry will depend on Smart Grid (SG) to a great degree in the future. It provides qualitative and quantitative services for better management of energy. Electrical devices such as Advanced Metering Infrastructure (AMI) and Smart Meter (SM) produce large data which is called Big Data. These Big Data is in the form of time series data that requires complex data analytics for prediction of consumption of energy. Prediction of consumption of energy using Big Data analytics can help to balance the supply and demand of energy which is one of the challenging task of SG. The researchers have covered these topics however, they have not tuned the parameters with optimization algorithm such as Genetic Algorithm (GA) for time series data. They have not analysed the prediction of energy using the Prophet model, data anomaly detection techniques and filtering techniques with respect to large time series data in SG. In the first scheme, GA is applied for tuning the parameters of Long Short Term Memory (LSTM). GA is an evolutionary process which is used for optimization. LSTM memorises values over arbitrary intervals which are capable to manage time series data. GA is combined with LSTM in order to process hyper-parameters such as hidden layers, epochs, data intervals, batch size and activation functions. Hence, GA creates a new vector for optimum solution that provides minimum error. These methods provide better results when compared with existing benchmarks. Moreover, GA-LSTM is used in a multi-threaded environment which will increase the speed of convergence. In the second scheme, various filtering techniques are used to predict the energy forecast which can improve the quality of service to the users. The filtering techniques primary task is to handle non-linearity in the input dataset. Various filtering techniques reduce the redundant data for energy consumption prediction.