Open AccessPrediction interval estimations for electricity demands and prices: a multi‐objective approach
Author(s) -
Shrivastava Nitin Anand,
Panigrahi Bijaya Ketan
Publication year - 2015
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.2014.0599
Subject(s) - interval (graph theory) , electricity , interval data , interval arithmetic , computer science , econometrics , electricity market , mathematical optimization , mathematics , data mining , engineering , electrical engineering , measure (data warehouse) , combinatorics , mathematical analysis , bounded function
Electricity price and demand forecasting are becoming essential practices for the deregulated market participants such as system operators, generation companies, industries and end use consumers. With a prominent growth in the uncertainty aspect of the energy sources, climatic changes and demand patterns, it is essential to supplement the traditional point forecasts with prediction intervals (PIs) which are an important tool for quantifying the uncertainty of forecasted entities. This study proposes a novel approach for generation of PIs using a differential evolution‐based multi‐objective approach. The traditional PI generation is framed as a multi‐objective problem and a set of Pareto‐optimal solutions are generated. The proposed technique is validated using electricity price and demand data from the Ontario electricity market. Experimental results indicate that the proposed technique can successfully generate high‐quality PIs.