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

1. Problem Statement

The integration of renewable energy sources (RES), such as photovoltaic (PV) panels, and flexible loads like heat pumps and electric vehicles into building energy systems has introduced significant complexity. This complexity arises from volatile power generation, dynamic electricity tariffs, and the coupling of thermal and electrical sectors. Traditional control methods, such as Model Predictive Control (MPC), rely on explicit system models and forecasts, which can be computationally intensive and difficult to maintain in uncertain environments. While Deep Reinforcement Learning (DRL) offers a promising data-driven alternative capable of learning optimal policies without explicit models, its "black-box" nature hinders user trust and practical adoption in critical infrastructure. There is a lack of frameworks that not only optimize energy management but also provide transparent, actionable insights into the decision-making process, particularly when validated on real-world empirical data rather than solely synthetic datasets.

2. Methodology

The authors propose a unified framework for Explainable Deep Reinforcement Learning (XRL) applied to residential building energy management. The methodology consists of three interconnected modules:

A. Data Ingestion and Forecast Validation

• Data Sources: The study utilizes two datasets:
  1. Empirical Data: Real-world measurements from the Living Lab Energy Campus (LLEC) at the Karlsruhe Institute of Technology (KIT), collected between June and September 2024. This includes residential demand, PV generation, grid electricity, and weather data.
  2. Synthetic Data: Generated using EnergyPlus (specifically the Generator_PVWatts model) to simulate a full year of building energy behavior under varying weather conditions.
• Forecasting: A Long Short-Term Memory (LSTM) network is employed to forecast demand and electricity prices. Comparative analysis determined that a 1-hour prediction horizon offers the best accuracy and stability.
• State Space: The state representation is expanded to include historical measurements (demand, PV, battery power, State of Charge - SoC), external signals (dynamic prices, weather), calendrical indicators, and short-term forecasts (demand and price).

B. Policy Learning and Benchmark Protocol

• Algorithms: Six DRL algorithms were trained and compared:
  • On-policy: Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO).
  • Off-policy: Deep Q-Network (DQN), Soft Actor-Critic (SAC), Twin Delayed Deep Deterministic Policy Gradient (TD3), Deep Deterministic Policy Gradient (DDPG).
• Environment: The agent controls battery charging/discharging currents (continuous action space for most algorithms, discrete for DQN) to maximize economic profit while respecting physical constraints (e.g., avoiding overcharging).
• Reward Function: The reward is defined by the economic trade-off between grid interaction costs and net demand costs, with penalties for unsafe actions (e.g., violating SoC limits).
• Hyperparameter Optimization: An automated search using the Optuna library was conducted to identify optimal hyperparameters for each algorithm.

C. Policy Interpretation (XRL)

To address the black-box issue, the authors employ post-hoc interpretation techniques:

1. Decision Tree Approximation (Policy Distillation): The learned DRL policies are approximated by decision trees to extract human-readable rules. This provides structural transparency regarding how state variables influence actions (Charge, Discharge, Idle).
2. Feature Removal Analysis: Specific state features (e.g., SoC, PV, price forecasts) are excluded during training to quantify their impact on cumulative rewards, thereby identifying dominant drivers of the policy.

3. Key Contributions

The paper outlines four primary contributions:

1. Integrated XRL Pipeline: A structured framework linking DRL policy outputs to explainability mechanisms (decision trees and feature ablation) specifically adapted for high-dimensional building energy state spaces.
2. Comparative Evaluation Protocol: A rigorous benchmarking of multiple DRL algorithms (both on-policy and off-policy) under identical environmental constraints.
3. Enhanced State-Space Design: The incorporation of both historical measurements and short-term forecast information, with the forecast horizon validated through quantitative error analysis.
4. Quantitative Insight: Identification of dominant state variables influencing policy formation and an analysis of how forecast information shapes control strategies.

4. Results

• Algorithm Performance: On-policy algorithms, specifically A2C and PPO, outperformed off-policy methods (DQN, SAC, TD3, DDPG) in terms of cumulative rewards and policy stability on both real-world and synthetic datasets.
  • Reasoning: The authors speculate that on-policy methods benefit from using fresh trajectories aligned with current forecast conditions, whereas off-policy methods may suffer from bias due to replay buffers containing outdated forecast data.
  • A2C Performance: A2C achieved the highest cumulative reward (568.49 EUR profit gain) on the empirical test set, significantly outperforming the baseline (0 EUR) and other algorithms.
• Interpretability Findings:
  • Decision Trees: The distilled decision trees revealed that State of Charge (SoC) is the primary driver for triggering actions. If SoC > 90%, the agent discharges; otherwise, it evaluates price forecasts and humidity.
  • Feature Importance: Feature removal analysis confirmed that SoC, PV generation, and price forecasts are critical. Interestingly, while current demand was found less critical for on-policy algorithms (as it must be met regardless), forecasted demand and price were essential for optimizing battery preparation.
  • Generalization: Policies trained on empirical data demonstrated robust performance when evaluated on the synthetic dataset, with on-policy agents consistently outperforming off-policy ones.
• Baseline Comparison: The XRL framework reduced operational costs compared to a baseline controller (derived from empirical data rules) and provided transparent decision logic.

5. Significance and Claims

The paper claims that the proposed XRL framework successfully bridges the gap between the high performance of black-box DRL and the transparency required for practical adoption in energy systems.

• Trust and Adoption: By providing transparent, actionable insights (via decision trees and feature analysis), the framework increases trust among domain experts and building operators, facilitating the adoption of AI-driven energy management.
• Regulatory Compliance: The authors note that such transparency is increasingly required by regulations like the European Union's AI Act.
• Complement to MPC: The framework is presented not as a replacement for Model Predictive Control (MPC) but as a complementary, fully data-driven alternative that can be updated through retraining rather than model redesign. Furthermore, the interpretable rules derived from XRL can inform or simplify MPC formulations.
• Scalability: The data-driven nature allows for potential transfer learning, where policies trained on one building can be adapted to others.

The authors maintain a modest tone, acknowledging that while the distilled decision trees provide valuable insights, they are approximations and cannot fully capture the nuance of the full DRL policy. The work focuses on a single building but suggests future directions for grid-level coordination and emergency control.