Week-ahead Forecasting of Household Energy Consumption Using CNN and Multivariate Data

Short-Term Load Forecasting for buildings has gained a lot of importance in recent times due to the ongoing penetration of renewable energy and the upgradation of power system networks to Smart Grids embedded with smart meters. Power System expansion is not able to keep pace with the energy consumption demands. In this scenario, accurate household energy forecasting is one of the key solutions to managing the demand side energy. Even a small percentage of improvement in forecasting error, translates to a lot of saving for both producers and consumers. In this paper, it was found out that Aggregated 1-Dimensional Convolutional Neural Networks can be effectively modeled to predict the household consumption with greater accuracy than a basic 1-Dimensional Convolutional Neural Network model or a classical Auto Regressive Integrated Moving Average model. The proposed Aggregated Convolutional Neural Network model was tested on a 4 year household energy consumption dataset and gave very promising Root Mean Square Error reduction