Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models.
In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data.
In order for the results to be comparable between different households, this study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications.
Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.