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Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms

Nawazish Alia, Rachael Shawb, Karl Mason

TL;DR

The paper addresses the challenge of managing energy-heavy dairy farm operations amid renewable intermittency and dynamic tariffs. It presents Forecast Aware PPO (F-PPO) with an adaptive PID-KL controller to schedule battery storage and water heater usage under realistic constraints, grounded in real farm data. The approach integrates short-term demand and generation forecasts into the RL observation space and uses an adaptive KL term to stabilize policy updates, achieving cost reductions and reduced grid imports compared to PPO, DQN, and SAC baselines. Results show notable improvements in both battery and heater scheduling, demonstrating scalable, proactive energy management with potential for broader adoption in sustainable agricultural operations. The work highlights a practical path toward lower energy costs and higher renewable utilization in modern dairy farming, with future directions including multi-agent coordination and broader renewable integration.

Abstract

Dairy farming is an energy intensive sector that relies heavily on grid electricity. With increasing renewable energy integration, sustainable energy management has become essential for reducing grid dependence and supporting the United Nations Sustainable Development Goal 7 on affordable and clean energy. However, the intermittent nature of renewables poses challenges in balancing supply and demand in real time. Intelligent load scheduling is therefore crucial to minimize operational costs while maintaining reliability. Reinforcement Learning has shown promise in improving energy efficiency and reducing costs. However, most RL-based scheduling methods assume complete knowledge of future prices or generation, which is unrealistic in dynamic environments. Moreover, standard PPO variants rely on fixed clipping or KL divergence thresholds, often leading to unstable training under variable tariffs. To address these challenges, this study proposes a Deep Reinforcement Learning framework for efficient load scheduling in dairy farms, focusing on battery storage and water heating under realistic operational constraints. The proposed Forecast Aware PPO incorporates short term forecasts of demand and renewable generation using hour of day and month based residual calibration, while the PID KL PPO variant employs a proportional integral derivative controller to regulate KL divergence for stable policy updates adaptively. Trained on real world dairy farm data, the method achieves up to 1% lower electricity cost than PPO, 4.8% than DQN, and 1.5% than SAC. For battery scheduling, PPO reduces grid imports by 13.1%, demonstrating scalability and effectiveness for sustainable energy management in modern dairy farming.

Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms

TL;DR

The paper addresses the challenge of managing energy-heavy dairy farm operations amid renewable intermittency and dynamic tariffs. It presents Forecast Aware PPO (F-PPO) with an adaptive PID-KL controller to schedule battery storage and water heater usage under realistic constraints, grounded in real farm data. The approach integrates short-term demand and generation forecasts into the RL observation space and uses an adaptive KL term to stabilize policy updates, achieving cost reductions and reduced grid imports compared to PPO, DQN, and SAC baselines. Results show notable improvements in both battery and heater scheduling, demonstrating scalable, proactive energy management with potential for broader adoption in sustainable agricultural operations. The work highlights a practical path toward lower energy costs and higher renewable utilization in modern dairy farming, with future directions including multi-agent coordination and broader renewable integration.

Abstract

Dairy farming is an energy intensive sector that relies heavily on grid electricity. With increasing renewable energy integration, sustainable energy management has become essential for reducing grid dependence and supporting the United Nations Sustainable Development Goal 7 on affordable and clean energy. However, the intermittent nature of renewables poses challenges in balancing supply and demand in real time. Intelligent load scheduling is therefore crucial to minimize operational costs while maintaining reliability. Reinforcement Learning has shown promise in improving energy efficiency and reducing costs. However, most RL-based scheduling methods assume complete knowledge of future prices or generation, which is unrealistic in dynamic environments. Moreover, standard PPO variants rely on fixed clipping or KL divergence thresholds, often leading to unstable training under variable tariffs. To address these challenges, this study proposes a Deep Reinforcement Learning framework for efficient load scheduling in dairy farms, focusing on battery storage and water heating under realistic operational constraints. The proposed Forecast Aware PPO incorporates short term forecasts of demand and renewable generation using hour of day and month based residual calibration, while the PID KL PPO variant employs a proportional integral derivative controller to regulate KL divergence for stable policy updates adaptively. Trained on real world dairy farm data, the method achieves up to 1% lower electricity cost than PPO, 4.8% than DQN, and 1.5% than SAC. For battery scheduling, PPO reduces grid imports by 13.1%, demonstrating scalability and effectiveness for sustainable energy management in modern dairy farming.
Paper Structure (42 sections, 22 equations, 10 figures, 4 tables, 3 algorithms)

This paper contains 42 sections, 22 equations, 10 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review and Background
  3. Reinforcement Learning (RL)
  4. Deep Reinforcement Learning
  5. Deep Q Network(DQN)
  6. Proximal Policy Optimization (PPO)
  7. Discrete Soft Actor-Critic (SAC)
  8. Applications of Energy Management
  9. Optimization-Based Methods for Energy Management
  10. Reinforcement Learning Methods for Residential and Microgrid Systems
  11. Deep Reinforcement Learning Methods for Energy Management
  12. Federated and Multi-Agent Reinforcement Learning Approaches for Energy Management
  13. Methodology
  14. Overview of the Proposed RL Based Energy Scheduling Methods
  15. Environment Design
  16. ...and 27 more sections

Figures (10)

  • Figure 1: The flow diagram of the Deep Reinforcement Learning process.
  • Figure 2: Overview of the system environment.
  • Figure 3: Comparison of load imported from the grid by different algorithms.
  • Figure 4: Agent behavior for battery charging and discharging during the day.
  • Figure 5: Training reward of implemented PPO algorithm
  • ...and 5 more figures