From High-resolution Data to High-resolution Probabilistic Load Forecasts

One of the contributions of my TSG2014 paper is to show that hourly data helps generate more accurate long term forecasts than those from daily or monthly data. While we showed the improvement in point load forecast accuracy, we did not formally compare probabilistic forecast accuracy. This conference paper completed that missing comparison. We will present the paper at the IEEE PES T&D conference this May. The working paper is available HERE.

Citation
Jingrui Xie; Tao Hong and Chongqing Kang "From high-resolution data to high-resolution probabilistic load forecasts", 2016 IEEE PES Transmission and Distribution Conference and Exposition, Dallas, TX, May 2-5, 2016

From High-resolution Data to High-resolution Probabilistic Load Forecasts

Jingrui Xie, Tao Hong and Chongqing Kang

Abstract

Long term load forecasting plays a vital role in power systems planning and utility financial planning. Traditional methods in long term load forecasting rely on monthly data, which offers limited observations to support the comprehensive models with sufficient explanatory variables to capture the salient features in the electricity demand series. The grid modernization efforts undertaken by many utilities over the past decade have made high-resolution data available for many analytical tasks including load forecasting. In this paper, we investigate the effectiveness of using high-resolution data in long term probabilistic load forecasting. The primary error measure we use for forecast evaluation is pinball loss function. Through a case study based on the data from a U.S. utility, we show that high-resolution data is beneficial to the improvement of probabilistic load forecasts.