Deep learning based ensemble method for household energy demand forecasting of smart home

Abstract

Electricity/Energy demand forecasting enables efficient electricity distribution through the use of smart grid. For the construction of such devices, we need to equip our homes with smart electricity probing devices that can record the electricity usage of our homes. When multiple homes are equipped with such devices, the aggregate electricity usage data can be obtained and consequently future electricity demand of a city can be predicted through the use of machine learning models on the data. As a result, electricity distribution can be adjusted according to consumer needs through the use of smart grid technology. In this paper, household electricity consumption data of a single household has been analyzed. Exploratory Data Analysis (EDA) is carried out on the data, time-series analysis is performed and time-series forecasting models such as Autoregressive Integrated Moving Average (ARIMA) model is used to make electricity demand predictions. A Recurrent Neural Network (RNN) model with Long Short Term Memory (LSTM) units has also been trained using the data. Multi-variate and univariate linear regression models have been developed using the dataset. Finally, to obtain a more reliable and accurate household energy consumption prediction model, a Mahalanobis distance based ensemble is created out of the 4 aforementioned models.