A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning

• URL: http://arxiv.org/abs/2403.09499v3
• Date: Wed, 15 May 2024 17:11:35 GMT
• Title: A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
• Authors: Nawazish Ali, Abdul Wahid, Rachael Shaw, Karl Mason,
• Abstract summary: This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting. The proposed algorithm reduces the cost of imported electricity from the grid by 13.41%, peak demand by 2%, and 24.49% when utilizing wind generation.
• Score: 3.1498833540989413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dairy farming consumes a significant amount of energy, making it an energy-intensive sector within agriculture. Integrating renewable energy generation into dairy farming could help address this challenge. Effective battery management is important for integrating renewable energy generation. Managing battery charging and discharging poses significant challenges because of fluctuations in electrical consumption, the intermittent nature of renewable energy generation, and fluctuations in energy prices. Artificial Intelligence (AI) has the potential to significantly improve the use of renewable energy in dairy farming, however, there is limited research conducted in this particular domain. This research considers Ireland as a case study as it works towards attaining its 2030 energy strategy centered on the utilization of renewable sources. This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting. This research also explores the effect of the proposed algorithm by adding wind generation data and considering additional case studies. The proposed algorithm reduces the cost of imported electricity from the grid by 13.41%, peak demand by 2%, and 24.49% when utilizing wind generation. These results underline how reinforcement learning is highly effective in managing batteries in the dairy farming sector.

Related papers

• A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization [1.2289361708127877]
  This research investigates the application of Proximal Policy Optimization to enhance dairy farming battery management. We evaluate the algorithm's effectiveness based on its ability to reduce reliance on the electricity grid.
  arXiv  Detail & Related papers  (2024-07-01T12:46:09Z)
• Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms [1.2289361708127877]
  This study aims to decrease dairy farms' dependence on traditional electricity grids by enabling the sale of surplus renewable energy in Peer-to-Peer markets. The Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES) has been developed, providing a platform to experiment with Reinforcement Learning techniques. The simulations demonstrate significant cost savings, including a 43% reduction in electricity expenses, a 42% decrease in peak demand, and a 1.91% increase in energy sales.
  arXiv  Detail & Related papers  (2024-05-21T12:19:17Z)
• Reinforcement Learning for Battery Management in Dairy Farming [3.441021278275805]
  This research paper utilizes Q-learning to learn an effective policy for charging and discharging a battery within a dairy farm setting. The results demonstrate that the developed policy significantly reduces electricity costs compared to the established baseline algorithm.
  arXiv  Detail & Related papers  (2023-08-17T14:52:15Z)
• Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach [48.18355658448509]
  Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy. We propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities.
  arXiv  Detail & Related papers  (2023-04-17T02:12:30Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design [0.0]
  This challenge belongs to a series started in 2019 under the name "Learning to run a power network" (L2RPN) We introduce new more realistic scenarios proposed by RTE to reach carbon neutrality by 2050. We provide a baseline using state-of-the-art reinforcement learning algorithm to stimulate the future participants.
  arXiv  Detail & Related papers  (2022-07-21T06:56:46Z)
• Machine learning applications for electricity market agent-based models: A systematic literature review [68.8204255655161]
  Agent-based simulations are used to better understand the dynamics of the electricity market. Agent-based models provide the opportunity to integrate machine learning and artificial intelligence. We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
  arXiv  Detail & Related papers  (2022-06-05T14:52:26Z)
• Compute and Energy Consumption Trends in Deep Learning Inference [67.32875669386488]
  We study relevant models in the areas of computer vision and natural language processing. For a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated.
  arXiv  Detail & Related papers  (2021-09-12T09:40:18Z)
• Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning [69.68068088508505]
  A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems. In this work, we use the deep deterministic policy algorithm to optimise the charging and discharging behaviour of a battery within such a system.
  arXiv  Detail & Related papers  (2021-09-10T10:59:14Z)
• Study of a Hybrid Photovoltaic-Wind Smart Microgrid using Data Science Approach [1.3764085113103222]
  A smart microgrid was implemented in Paracas, Ica, Peru, composed of 6kWp PV + 6kW Wind and provides electricity to a rural community of 40 families. Real data of solar irradiance, wind speed, energy demand, and voltage of the battery bank from 2 periods of operation were studied to find patterns, seasonality, and existing correlations between the analyzed data. These analyzed data will be used to improve sizing techniques and provide recommendations for energy management to optimize the performance of smart microgrids.
  arXiv  Detail & Related papers  (2021-05-14T01:31:23Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.