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

arXiv: https://arxiv.org/abs/2601.08052 · HTML: https://mlsanity.com/paper/2601.08052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret](https://mlsanity.com/paper/2503.01491/) (2025)
- [Deep Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/1909.10165/) (2019)
- [Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management](https://mlsanity.com/paper/2301.00641/) (2022)
- [A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resources](https://mlsanity.com/paper/2211.03430/) (2022)
- [Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice](https://mlsanity.com/paper/1512.00408/) (2015)
- [Modern Deep Reinforcement Learning Algorithms](https://mlsanity.com/paper/1906.10025/) (2019)
- [Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures](https://mlsanity.com/paper/2111.11868/) (2021)
- [Modelling Electricity Consumption in Irish Dairy Farms Using Agent-Based Modelling](https://mlsanity.com/paper/2308.09488/) (2023)
- [A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning](https://mlsanity.com/paper/2403.09499/) (2024)
- [Machine Learning Approaches to Predict and Detect Early-Onset of Digital Dermatitis in Dairy Cows using Sensor Data](https://mlsanity.com/paper/2309.10010/) (2023)
- [Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning](https://mlsanity.com/paper/2507.21153/) (2025)
- [Sizing Storage for Reliable Renewable Integration: A Large Deviations Approach](https://mlsanity.com/paper/1904.04771/) (2019)

## Abstract (arXiv)

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.