Due to a lack in the current technology infrastructure, many farmers, industry professionals and policy makers are unsure of the most suitable energy technology for specific agricultural applications.

Project Insights

  • €91,317

    Total Project Costs
  • 2 yr

    Project Duration
  • 2018

    Year Funded

Project Description

The agri-food sector is Ireland's largest indigenous manufacturer and export industry accounting for 23% of all manufacturing turnover and 10.7% of all exports. Government policy has put forward ambitious targets for the agri-food sector, aiming for very large increases in overall production and an 85% increase in exports by 2025. However, the recent increase in food production has resulted in increased energy usage and GHG emissions with agriculture accounting for 33% of national GHG production. As food production with corresponding energy consumption and GHG emissions rapidly increase, the selection and proliferation of optimal renewable energy and energy efficient technologies for the agri-food sector is essential for Ireland to meet its future energy and environmental obligations. This project will tackle this problem by developing the Agricultural Energy Optimisation Platform (AEOP), which will enable farmers, industry professionals and policy makers to select the most beneficial energy technologies for specific agricultural applications. AEOP will combine new beef and sheep energy models with an existing dairy energy model. These new models will be created and validated using empirical data collected on commercial farms. Once all three models are developed, the next step will be to add automatic optimisation functionality. Optimisation algorithms and machine learning techniques will be applied to the platform, which will solve complicated problem spaces to find the optimal energy technology application based on the user's input criteria such as minimum renewable energy percentage, tons of CO2 offset, energy savings, etc. The finished product will be a national agricultural energy optimisation tool.

Project Details

Total Project Cost: €91,317

Funding Agency: SEAI

Year Funded: 2018

Lead Organisation: Cork Institute of Technology

Partner Organisation(s): Teagasc

Michael D. Murphy | Lead Researcher(s)