The Powerful Use of AI in the Energy Sector: Intelligent Forecasting

• URL: http://arxiv.org/abs/2111.02026v1
• Date: Wed, 3 Nov 2021 05:30:42 GMT
• Title: The Powerful Use of AI in the Energy Sector: Intelligent Forecasting
• Authors: Erik Blasch, Haoran Li, Zhihao Ma, Yang Weng
• Abstract summary: This paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector. The goal is to provide a high level of confidence to energy utility users.
• Score: 7.747343962518897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) techniques continue to broaden across governmental and public sectors, such as power and energy - which serve as critical infrastructures for most societal operations. However, due to the requirements of reliability, accountability, and explainability, it is risky to directly apply AI-based methods to power systems because society cannot afford cascading failures and large-scale blackouts, which easily cost billions of dollars. To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model. The goal is to provide a high level of confidence to energy utility users. For illustration purposes, the paper uses power system event forecasting (PEF) as an example, which carefully analyzes synchrophasor patterns measured by the Phasor Measurement Units (PMUs). Such a physical understanding leads to a data-driven framework that reduces the dimensionality with physics and forecasts the event with high credibility. Specifically, for dimensionality reduction, machine learning arranges physical information from different dimensions, resulting inefficient information extraction. For event forecasting, the supervised learning model fuses the results of different models to increase the confidence. Finally, comprehensive experiments demonstrate the high accuracy, efficiency, and reliability as compared to other state-of-the-art machine learning methods.

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