Open AccessWeather biased optimal delta model for short‐term load forecast
Author(s) -
Uppal Manish,
Kumar Garg Vijay,
Kumar Dinesh
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0331
Subject(s) - mean absolute percentage error , benchmark (surveying) , mean squared error , econometrics , seasonality , term (time) , computer science , grid , electricity , standard deviation , forecast period , statistics , economics , mathematics , engineering , finance , geography , cash flow , physics , geometry , geodesy , cash flow statement , quantum mechanics , electrical engineering
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.