Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Afzaal, Muhammad Umara | Sajjad, Intisar Alib; * | Khan, Muhammad Faisal Nadeemb | Haroon, Shaikh Saaqibb | Amin, Salmanb | Bo, Ruic | ur Rehman, Waqasc
Affiliations: [a] Assistant Engineer Electrical, Operations and Maintenance Division, KOENERGY Korea for Gulpur Hydro Power Project, Pakistan | [b] Department of Electrical Engineering, University of Engineering and Technology Taxila, Pakistan | [c] Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla MO, USA
Correspondence: [*] Corresponding author. Intisar Ali Sajjad, Department of Electrical Engineering, University of Engineering and Technology Taxila, Pakistan. E-mail: [email protected]. ORCID: 0000-0002-8947-9729
Abstract: The characterization of electrical demand patterns for aggregated customers is considered as an important aspect for system operators or electrical load aggregators to analyze their behavior. The variation in electrical demand among two consecutive time intervals is dependent on various factors such as, lifestyle of customers, weather conditions, type and time of use of appliances and ambient temperature. This paper proposes an improved methodology for probabilistic characterization of aggregate demand while considering different demand aggregation levels and averaging time step durations. At first, a probabilistic model based on Weibull distribution combined with generalized regression neural networks (GRNN) is developed to extract the inter-temporal behavior of demand variations and, then, this information is used to regenerate aggregate demand patterns. Average Mean Absolute Percentage Error (AMAPE) is used as a statistical indicator to assess the accuracy and effectiveness of proposed probabilistic modeling approach. The results have demonstrated that the performance of proposed approach is better in comparison with an existing Beta distribution-based method to characterize aggregate electrical demand patterns.
Keywords: Electrical demand characterization, generalized regression neural networks, scenario generations, time series, Weibull probability distribution
DOI: 10.3233/JIFS-200462
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4491-4503, 2020
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]