Electric load forecasting with recency effect: A big data approach

Temperature plays a key role in driving electricity demand. We adopt "recency effect", a term originated from psychology, to denote the fact that electricity demand is affected by the temperatures of preceding hours. In the load forecasting literature, the temperature variables are often constructed in the form of lagged hourly temperatures and moving average temperatures. Over the past decades, computing power has been limiting the amount of temperature variables that can be used in a load forecasting model. In this paper, we present a comprehensive study on modeling recency effect through a big data approach. We take advantage of the modern computing power to answer a fundamental question: how many lagged hourly temperatures and/or moving average temperatures are needed in a regression model to fully capture recency effect without compromising the forecasting accuracy? Using the case study based on data from the load forecasting track of the Global Energy Forecasting Competition 2012, we first demonstrate that a model with recency effect outperforms its counterpart (a.k.a., Tao’s Vanilla Benchmark Model) in forecasting the load series at the top (aggregated) level by 18% to 21%. We then apply recency effect modeling to customize load forecasting models at low level of a geographic hierarchy, again showing the superiority over the benchmark model by 12% to 15% on average. Finally, we discuss four different implementations of the recency effect modeling by hour of a day.