Open AccessDistribution feeder‐level day‐ahead peak load forecasting methods and comparative study
Author(s) -
Chen Tairen,
Lehr Jane M.,
Lavrova Olga,
MartínezRamón Manel
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.1745
Subject(s) - probabilistic forecasting , computer science , linear regression , regression analysis , bayesian probability , electrical load , econometrics , statistics , regression , engineering , mathematics , machine learning , artificial intelligence , probabilistic logic , voltage , electrical engineering
Day‐ahead peak load forecasting is a crucial factor for the electrical utility for daily power planning and distribution. A comparative study of the peak load forecasting at distribution feeder‐level circuits with high penetration level of PV generation are investigated. First, two years of private load data from the local utility and two years of public load data from Texas utility are analysed. The correlation analysis is applied to peak load and its driver factors. Next, Bayesian additive regression trees (BART) is employed to do peak load forecasting. Since the BART method takes an amount of time to generate the forecasting, the composite kernel methods based on Gaussian process regression (CKGPR) are designed. Then these methods are compared with multiple linear regression method and the support vector regression method based on the residential area and the business area load data. Thorough comparison results are presented based on five forecasting measurements. The BART has the best forecasting accuracy among all the indices, and the CKGPR also has counterpart forecasting results but with less computation time. Meanwhile, the forecasting accuracy difference between two areas is analysed. Lastly, influential driver factors are summarised.