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Big Data Driven Hybrid AI Method for Energy Demand Forecasting and Operational Planning in Industrial Greenhouse Production

Project Description

This project aims to develop a big data driven hybrid AI method for forecasting the energy demand of industrial greenhouse plant production. Energy demand forecast provide necessary information for planning and optimizing the operation of a grid connected local energy system that is based on distributed renewable energy resources. The application of the method provides the means for optimizing the operation of the local energy system and the operational planning of the interaction with the main grid in response to different energy pricing models.

Industrial greenhouses are chosen in this project because the greenhouse production has a high degree of flexibility in using different energy sources. The production (the plants) also tolerates some degree of dynamics in the climate. Combining the flexibility and the production tolerance with the right decisions, the growers have the possibility to be an active player in the energy market. To develop the big data driven hybrid AI framework method for industrial load forecasting and operational planning, industrial greenhouse energy consumption, its distributed energy resources (e.g., combined heat and power), control strategies and production flow will be investigated and applied in the project with a set of mixed big data.

Project Summary
Project period September 2021 to August 2024
Total budget DKK 1,100,00
Funding agency SDC
Organization Managing the Project SDU Center for Energy Informatics
Supervisors Bo Nørregaard Jørgensen
Zheng Grace Ma
Carl-Otto Ottosen (Aarhus University)
Fulin Wang (Tsinghua University)
Changqing Tian (Chinese Academy of Sciences)
PhD researcher Nicolai Bo Vanting

Sidst opdateret: 06.01.2023