The field of energy research is moving towards developing more resilient and equitable systems. Researchers are focusing on creating frameworks that can predict and optimize energy distribution, taking into account factors such as weather events, cyber threats, and social disparities. The use of artificial intelligence, machine learning, and data-driven approaches is becoming increasingly prevalent in this area. Noteworthy papers include: Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting, which integrates Bayesian regime detection with conditional neural processes for 24-hour electricity price prediction. Uncertainty-aware Predict-Then-Optimize Framework for Equitable Post-Disaster Power Restoration proposes an equity-aware power restoration strategy that balances restoration efficiency and equity across communities.
Energy Resilience and Forecasting
Sources
Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting
Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets
Agentic-AI based Mathematical Framework for Commercialization of Energy Resilience in Electrical Distribution System Planning and Operation
Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions
Uncertainty-aware Predict-Then-Optimize Framework for Equitable Post-Disaster Power Restoration
PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting
Preparing for the worst: Long-term and short-term weather extremes in resource adequacy assessment