Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings

Researchers developed an explainable deep reinforcement learning framework for optimizing energy management in buildings with renewable sources, battery storage, and dynamic pricing. Testing on real-world data from KIT's Living Lab Energy Campus showed that on-policy algorithms (A2C, PPO) outperformed off-policy methods while providing transparent insights into decision-making processes.

This research addresses a critical gap in the adoption of AI-driven energy management systems. While deep reinforcement learning offers powerful optimization capabilities for complex building energy systems, its black-box nature has created barriers to real-world deployment. The framework presented bridges this gap by combining advanced control algorithms with explainability techniques, making AI decisions interpretable to building operators and stakeholders.

The context reflects broader industry trends. Renewable energy integration, dynamic electricity markets, and distributed energy resources have fundamentally changed building operations from static to dynamic optimization problems. Traditional rule-based controls struggle with volatile solar generation, time-varying electricity prices, and heat pump complexity. The comparison between on-policy (A2C, PPO) and off-policy algorithms demonstrates that more stable, interpretable learning approaches outperform complex off-policy methods in this domain.

For energy stakeholders and building operators, this work has immediate practical implications. The demonstrated cost reductions through optimal battery management directly impact operating expenses, while transparent decision-making reduces adoption friction among facility managers and utility companies. The expansion of state space—incorporating weather forecasts, price signals, demand predictions, and calendar information—reflects how effective energy management requires comprehensive contextual understanding.

Looking forward, the intersection of AI explainability and energy systems represents a significant emerging market. As buildings become increasingly automated and grid services more sophisticated, the demand for trustworthy AI controls will grow. This research validates that explainability doesn't sacrifice performance, removing a key objection to AI deployment in critical infrastructure. The methodology could extend to district-level energy management and grid services, amplifying its potential impact.

• →On-policy DRL algorithms (A2C, PPO) achieved better performance and stability than off-policy methods for building energy optimization.
• →Post-hoc interpretation techniques successfully explained learned control policies without sacrificing optimization performance.
• →Real-world testing on KIT's Living Lab demonstrated measurable electricity cost reductions through intelligent battery management.
• →Explainable AI framework removes adoption barriers by making autonomous energy decisions transparent to building operators.
• →Comprehensive state space incorporating forecasts and dynamic pricing proved essential for effective reinforcement learning in complex energy environments.