Short Term Load Forecasts of Low Voltage Demand and the Effects of Weather

TL;DR

This paper evaluates various short-term load forecasting methods for low voltage electricity demand, emphasizing the impact of weather, especially temperature, on forecast accuracy using real LV feeder data.

Contribution

It provides a comprehensive analysis of forecasting methods and examines how actual and forecasted temperature influence load prediction accuracy at the LV level.

Findings

Weather, particularly temperature, significantly affects forecast accuracy.

Empirical comparison highlights key drivers of LV load forecasting performance.

Analysis informs better management of LV networks with weather considerations.

Abstract

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, previous studies have mostly focused on forecasting at the individual household or building level using data from smart meters. In this study we provide detailed analysis of a variety of methods in terms of both point and probabilistic forecasting accuracy using data from 100 real LV feeders. Moreover, we investigate the effect of temperature (both actual and forecasts) on the accuracy of load forecasts. We present some important results on the drivers of LV forecasting accuracy that are crucial for the management of LV networks, along with an empirical comparison of forecast measures.