Short term load forecasting and the effect of temperature at the low voltage level

Short term load forecasts will play a key role in the implementation of smart electricity grids. They are required to optimise a wide range of potential network solutions on the low voltage (LV) grid, including integrating low carbon technologies (such as photovoltaics) and utilising battery storage devices. Despite the need for accurate LV level load forecasts much of the literature has focused on the individual household or building level using data from smart meters or on aggregates of such data. In this study we provide detailed analysis of several state-of-the-art methods for both point and probabilistic LV load forecasts. We evaluate the out-of-sample forecast accuracy of these methodologies on 100 real LV feeders, for horizons from one to four days ahead. In addition, we also test the effect of temperature (both actual and forecast) on the accuracy of load forecasts. We present some important results on the drivers of forecasts accuracy as well as the empirical comparison of point and probabilistic forecast measures