Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System

John O’Donnell () and Wencong Su
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John O’Donnell: DTE Electric, Detroit, MI 48226, USA
Wencong Su: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA

Energies, 2023, vol. 16, issue 15, 1-21

Abstract: Greater variation in electrical load should be expected in the future due to the increasing penetration of electric vehicles, photovoltaics, storage, and other technologies. The adoption of these technologies will vary by area and time, and if not identified early and managed by electric utilities, these new customer needs could result in power quality, reliability, and protection issues. Furthermore, comprehensively studying the uncertainty and variation in the load on circuit elements over periods of several months has the potential to increase the efficient use of traditional resources, non-wires alternatives, and microgrids to better serve customers. To increase the understanding of electrical load, the authors propose a multistep, attention-focused, and efficient machine learning process to provide probabilistic forecasts of distribution transformer load for several months into the future. The method uses the solar irradiance, temperature, dew point, time of day, and other features to achieve up to an 86% coefficient of determination (R 2 ).

Keywords: clustering methods; load forecasting; microgrid; neural networks; smart meters (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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