Weather biased optimal delta model for short‐term load forecast. Issue 6 (18th November 2020)

• Record Type: Journal Article
• Authors: Uppal, Manish; Kumar Garg, Vijay; Kumar, Dinesh
• Is Part Of: IET smart grid. Volume 3:Issue 6(2020)
• Page Start: 835
• Page End: 842
• Publication Date: 2020-11-18
• Subjects: power markets -- load forecasting -- mean square error methods -- demand side management -- power grids; Delhi -- uncertainty analysis -- electricity demand -- weather biased optimal delta model -- prevalent grid regulations -- root‐mean‐square error -- mean absolute percentage error -- statistical performance metrics -- benchmark model -- calendar variables -- seasonality trend -- one‐day‐lagged demand statistics -- day‐ahead load forecast -- electricity supply -- financial implications -- grid security -- technological evolutions -- unprecedented weather conditions -- demand variations -- power demand -- deregulated Indian electricity market -- short‐term load forecast; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0331
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : In the current scenario of the deregulated Indian electricity market where the power demand and its availability vary remarkably, the factors playing a significant role in demand variations are often associated with the impact of unprecedented weather conditions and technological evolutions. To maintain grid security and discipline that yield to financial implications, there lies a great need to formulate an equilibrium between electricity supply and demand. Devising a model to anticipate the variations which are highly adaptive to such changes is the need of the hour. For this purpose, an algorithm has been proposed in this study, which is best suited for the day‐ahead load forecast. The variables selected for the forecast are one‐day‐lagged demand statistics, seasonality trend, weather, and calendar variables. The proposed algorithm outperforms the existing benchmark model, which is evaluated through various statistical performance metrics such as mean absolute percentage error, mean absolute error, root‐mean‐square error, and coefficient of variation. The performance of the proposed methodology at the seasonal level is analysed and validated through uncertainty analysis with one post‐sample year for the state of Delhi, India. This model presents its compatibility to prevalent grid regulations as well as shall hold good in the weather and demand variations possibly expected in the future.