Abstract
AbstractThis paper presents hourly electrical load forecasting as time series forecasting model using Multilayer deep learning and Long Short-Term Memory neural network Technique and their detailed comparative study with various machine learning techniques based on their mean Squared Error (MSE), mean absolute percentage error (MAPE), root mean squared error (RMSE) and training time. Load Forecasting has immense potential to help in modulating the generation and distribution potentials of our smart grids in accordance to the requirement so that optimum power is generated and supplied through various channels which would be effective in grid management and operations.KeywordsHourly electrical load forecastingTime series forecastingLong short-term memory neural networkSmart gridsDeep learningMachine learningLong short term memory network (LSTM)Recurrent neural networks (RNN)Root mean square propagation (RMSProp)
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