Relative Humidity for Load Forecasting Models

Weather is a key driving factor of electricity demand. During the past five decades, temperature is the most commonly used weather variable in load forecasting models. Although humidity has been discussed in the load forecasting literature, it has not been studied as formally as temperature. Humidity is usually embedded in the form of heat index (HI) or temperature-humidity index. In this paper, we investigate how relative humidity (RH) affects electricity demand. From a real-world case study at a utility in North Carolina, we find that RH plays a vital role in driving electricity demand during the warm months (June to September). We then propose a systematic approach to including RH variables in a regression analysis framework, resulting in the recommendation of a group of RH variables. The proposed models with the recommended addition of RH variables improve the forecast accuracy of Tao's Vanilla benchmark model and its three derivatives in one-day (24 h) ahead, one-week ahead, one-month ahead, and one-year ahead ex post forecasting settings with the relative reduction in mean absolute percentage error ranging from 4% to 9% in this case study. It also outperforms two HI-based models under the same settings. Moreover, an extended test case also demonstrates the effectiveness of these RH variables on improving the artificial neural network models.