A study on probability of distribution loads based on expectation maximization algorithm

Ganjavi, Amin; Christopher, Edward; Johnson, Christopher Mark; Clare, Jon

Abstract

In a distribution power network, the load model has no certain pattern or predicted behaviour due to large range of data and changes in energy consumption for end-user consumers. Thus, a powerful analysis based on probabilistic structure is required. For this paper Gaussian Mixture Model (GMM) has been used. GMM is a powerful probability model that allows different types of load distributions to be presented as a combination of several Gaussian distributions. The parameters of GMM is unknown for large random data such as real load data and these parameters can be identified by Expectation Maximization (EM) algorithm. This paper presents a method to evaluate probabilistic load data concerning the time-evolution of any type of distribution load for any duration of time. The proposed method is explained through generated load data of 100 residential houses for duration of one year.

Citation

Ganjavi, A., Christopher, E., Johnson, C. M., & Clare, J. (2017, April). A study on probability of distribution loads based on expectation maximization algorithm. Presented at 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT, Washington, DC, USA

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT
• Start Date: Apr 23, 2017
• End Date: Apr 26, 2017
• Acceptance Date: Feb 14, 2017
• Publication Date: Oct 30, 2017
• Deposit Date: Aug 23, 2017
• Electronic ISSN: 2472-8152
• Peer Reviewed: Peer Reviewed
• Pages: 1-5
• Series ISSN: 2472-8152
• Book Title: IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017
• ISBN: 978-1-5386-2891-1
• DOI: https://doi.org/10.1109/ISGT.2017.8086037
• Keywords: Expectation Maximization, Gaussian Mixture Model, Load forecasting and Probability, Probability Density Function
• Public URL: https://nottingham-repository.worktribe.com/output/890985
• Publisher URL: http://ieeexplore.ieee.org/abstract/document/8086037/
• Related Public URLs: http://www.ieee-pes.org/meetings-and-conferences/conference-calendar/monthly-view/165-sponsored-by-pes/562-innovative-smart-grid-technologies-isgt-2017; http://sites.ieee.org/isgt-2017/
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• Contract Date: Aug 23, 2017