Predicting Hourly Residential Energy Consumption using Random Forest and Support Vector Regression : An Analysis of the Impact of Household Clustering on the Performance Accuracy

Sammanfattning: The recent increase of smart meters in the residential sector has lead to large available datasets. The electricity consumption of individual households can be accessed in close to real time, and allows both the demand and supply side to extract valuable information for efficient energy management. Predicting electricity consumption should help utilities improve planning generation and demand side management, however this is not a trivial task as consumption at the individual household level is highly irregular. In this thesis the problem of improving load forecasting is ad-dressed using two machine learning methods, Support Vector Machines for regression (SVR) and Random Forest. For a customer base consisting of 187 households in Austin, Texas, pre-dictions are made on three spatial scales: (1) individual house-hold level (2) aggregate level (3) clusters of similar households according to their daily consumption profile. Results indicate that using Random Forest with K = 32 clusters yields the most accurate results in terms of the coefficient of variation. In an attempt to improve the aggregate model, it was shown that by adding features describing the clusters’ historic load, the performance of the aggregate model was improved using Random Forest with information added based on the grouping into K = 3 clusters. The extended aggregate model did not outperform the cluster-based models. The work has been carried out at the Swedish company Watty. Watty performs energy disaggregation and management, allowing the energy usage of entire homes to be diagnosed in detail.