Description

Prediction of the energy consumption is a key aspect of home energy management systems, whose aim is to increase the occupant’s comfort while reducing the energy consumption. Therefore, power companies need to investigate models to better forecast and plan the energy use. One approach to address this problem is the estimation of energy consumption in the customer level. Energy consumption forecasting problem is a regression task. It consists of predicting the energy consumption for the next month given a finite history of a customer. Clustering the data using k-means, prediction using an ensemble of forecasts based on the historical median evaluation .A model ensemble technique is proposed which achieves excellent forecast results, comparing additionally very favourably with existing approaches. We are implementing machine learning algorithm (Linear Regression) and our proposed Deep learning algorithm (LSTM) give low error values and High accurate prediction.