  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.
