Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home

TL;DR

This paper compares three multivariate forecasting methods—ANN, SVR, and Fuzzy Systems—for predicting house electricity consumption, finding SVR to be the most accurate using Honda Smart Home data.

Contribution

It provides a comparative analysis of forecasting methods on multivariate data for residential energy consumption prediction.

Findings

Support Vector Regression outperforms ANN and Fuzzy Systems.

SVR achieves lower MAE and RMSE in electricity consumption forecasting.

Multivariate methods are essential for accurate building energy predictions.

Abstract

The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector Regression, and Fuzzy Rule-Based Systems for Regression by constructing many models for each method on a multivariate data set in different time terms. The comparison shows that SVR is a superior method over the alternatives.