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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Parsimonious Short-Term Load Forecasting for Optimal Operation Planning of Electrical Distribution Systems

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Author(s):
Lopez, Juan Camilo [1] ; Rider, Marcos J. [1] ; Wu, Qiuwei [2, 3]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Dept Energy & Syst, BR-13083852 Campinas, SP - Brazil
[2] Tech Univ Denmark, Ctr Elect & Energy, Dept Elect Engn, DK-2800 Lyngby - Denmark
[3] Harvard Univ, Sch Engn & Appl Sci, Harvard China Project, Cambridge, MA 02138 - USA
Total Affiliations: 3
Document type: Journal article
Source: IEEE Transactions on Power Systems; v. 34, n. 2, p. 1427-1437, MAR 2019.
Web of Science Citations: 2
Abstract

The optimal operation planning (OOP) of electrical distribution systems (EDS) is very sensible to the quality of the short-term load forecasts. Assuming aggregated demands in EDS as univariate non-stationary seasonal time series, and based on historical measurements gathered by smart meters, this paper presents a parsimonious short-term load forecasting method to estimate the expected outcomes of future demands, and the standard deviations of forecast errors. The chosen short-term load forecasting method is an adaptation of the multiplicative autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models are parsimonious forecasting techniques because they require very few parameters and low computational resources to provide an adequate representation of stochastic time series. Two approaches are used in this paper to estimate the parameters that constitute the proposed multiplicative ARIM Amodel: a frequentist and a Bayesian approach. Advantages and disadvantages of both methods are compared by simulating a centralized self-healing scheme of a real EDS that uses the forecasts to deploy a robust restoration plan. Results show that the proposed seasonal ARIMA model is a fast, precise, straightforward, and adaptable load forecasting method, suitable for OOP of highly supervised EDS. (AU)

FAPESP's process: 17/02196-0 - Stochastic estimation of maximum load to be restored by the self-healing system: a frequentist and Bayesian approach
Grantee:Juan Camilo Lopez Amezquita
Support Opportunities: Scholarships abroad - Research Internship - Doctorate