Quantile regression averaging‐based probabilistic forecasting of daily ambient temperature

Abstract

The inclusion of conductor temperature variations for numerous power system planning and operational studies has long been recognized in the literature. The conductor temperature is majorly affected by environmental factors such as the ambient temperature. An efficient forecasting technique for forecasting ambient temperature is the need of the hour to prevent unexpected hazards in power systems and other areas caused due to temperature variations. Numerous researches have proposed different point forecasting and probabilistic forecasting models for the forecasting of ambient temperature. The probabilistic forecasting of ambient temperature provides complete information about future uncertainties, therefore quantifying the effects of daily temperature variations. A forecast combination approach, such as the quantile regression averaging, has never been utilized in this context. The selection of suitable point forecasters that complement each other's effects by characterizing different aspects of the ambient temperature data for averaging and the use of frequency components that explain the daily and periodic seasonal variations of ambient temperature to construct a forecasting model are the significant features of this paper. The proposed model is used with four varieties, and each is compared with the others. It is found that the variant using all the complementary individual point forecasters performs better in making probabilistic forecasts than the other options as well as better than the popular quantile k-nearest neighbors, quantile egression forests, and basic quantile regression as inferred from the quantile score, Winkler score, and reliability plots.

A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting

“…The forecast performance in terms of quantile forecasts and interval forecasts (PIs) is measured by using the famous error metrics and scores such as MAE, RMSE, QS, and WS. 30,31,33,34,37,39 The proposed QkNNRA model is compared with some widely used probabilistic forecasting models present in the literature. The forecasting models used for comparison are briefly discussed underneath.…”

“…QRA has been very useful in forecasting electricity spot prices, 29 load power, 30 global horizontal irradiance, 31 shortterm hourly PV generation, 32 daily PV generation, 33 and daily ambient temperature. 34 A QRA model combines the readily available point forecasts to make probabilistic forecasts of the desired data. The forecast accuracy mainly depends on the choice of the individual point forecasters.…”