ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

• Published in: Energy (Oxford) Vol. 154; pp. 383 - 389
• Main Authors: Wu, Da-Chun, Amini, Amin, Razban, Ali, Chen, Jie
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.07.2018; Elsevier
• Subjects: algorithms; consumers (people); cost control; Electrical maximum demand; energy efficiency; half life; Load forecast; manufacturing; Peak shaving; POWER TRANSMISSION AND DISTRIBUTION; prediction; seasonal variation; Stochastic model; stochastic processes
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.04.117

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. The aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand. •Novel method to forecast the occurrence of daily maximum demand.•Electrical demand pattern can be regarded as a mean reversion process.•The algorithm requires only the historical demand data.•The algorithm works best on patterns with distinct peak occurrences.