Ceit – Decision-making support tool for CHP plants
Ceit
Sector: Energy
Business Case
Software tool that recommends, based on electricity price and thermal demand predictions, when to start the cogeneration engines in order to maximise the profit from the sale of electricity.
Objectives
Create a visual analytics tool that provides cogeneration plant operating personnel with access to plant KPIs and visual exploration of optimal startup/shutdown patterns of cogeneration engines.
Use case
Application of the tool in the cogeneration plant of Basurto Hospital. Machine Learning models to predict electricity price and thermal energy demand. Heuristic optimisation algorithm to recommend when to turn cogeneration engines on and off.
Infrastructure
Cloud
Technology
Automatic or Deep Learning
Data
The data used are electricity price time series and time series extracted from the cogeneration plant’s SCADA.
Resources
About 6 people participated in the project.
Difficulties and learning
The main difficulty of the project was to understand the needs of the person responsible for operating the cogeneration plant. It was crucial to know in detail the procedures followed and the tools used by the operator to make decisions (what data were used, how the operator accessed them, how he/she processed them, etc.). From there, the work consisted of automating these procedures and transforming the data to make it visually informative.
KPIs (business impact and metrics of the model)
TRL 7-9
Funding
Hazitek Competitive
Collaborators, Partners
Genelek Sistemas and Giroa-Veolia
