Day Ahead Electric Load Forecasting by an Intelligent Hybrid Model Based on Deep Learning for Smart Grid
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Electrical load forecasting is a challenging problem due to random and non-linear behavior of the consumers. With the emergence of the smart grid (SG) and advanced metering infrastructure (AMI), people are capable to record, monitor, and analyze such a complicated non-linear behavior. Electric load forecasting models are indispensable in the decision making, planning, and contract evaluation of the power system. In this regard, various load forecasting models are proposed in the literature, which exhibit trade-off between forecast accuracy and execution time (convergence rate). In this article, a fast and accurate short-term load forecasting model is proposed. The abstractive features from the historical data are extracted using modified mutual information (MMI) technique. The factored conditional restricted boltzmann machine (FCRBM) is empowered via learning to predict the electric load. Eventually, the proposed genetic wind driven optimization (GWDO) algorithm is used to optimize the performance. The remarkable advantages of the proposed framework are the improved forecast accuracy and convergence rate. The forecast accuracy is improved through the use of MMI technique and FCRBM model. On the other side, convergence rate is enhanced by GWDO algorithm. Simulation results illustrate that the proposed fast and accurate model outperforms existing models i.e., Bi-level, MI-artificial neural network (MI-ANN), and accurate fast converging short-term load forecast (AFC-STLF) in terms of forecast accuracy and convergence rate.
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