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

Stephen Haben, Georgios Giasemidis, Florian Ziel and Siddharth Arora

International Journal of Forecasting, 2019, vol. 35, issue 4, 1469-1484

Abstract: Short term load forecasts will play a key role in the implementation of smart electricity grids. They are required for optimising a wide range of potential network solutions on the low voltage (LV) grid, including the integration of low carbon technologies (such as photovoltaics) and the utilisation of 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. This study provides a detailed analysis of several state-of-the-art methods for both point and probabilistic LV load forecasts. We evaluate the out-of-sample forecast accuracies of these methodologies on 100 real LV feeders, for horizons from one to four days ahead. In addition, we also test the effect of the 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 on the empirical comparison of point and probabilistic forecast measures.

Keywords: Probabilistic load forecasting; Low voltage networks; Temperature effects; Short term load forecasting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1469-1484

DOI: 10.1016/j.ijforecast.2018.10.007

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