Digitization, decentralization and pressing need for decarbonization are accelerating the energy landscape transition faster than expected. Technological breakthroughs empower the citizen at the community level and smart energy consumption becomes the key component. However, the flexible consumers remain still the main challenge to establishes a smart energy community.
CLEF project contributes to the shift to a carbon-neutral energy system by enhancing the flexibility of (pro-) consumers at the residential areas with sharing the required information and providing an accurate and reliable forecasting.
The aim of the CLEF project is to develop an interactive communication and information platform and design a software tool to forecast energy production and consumption days ahead. Furthermore, CLEF-app provides recommendation for optimal planning in order to manage self-consumption of PV energy at community level.
CLEF-app connects to the weather data-provider database, downloads the weather forecast data and predicts PV energy generation.
Our AI based software pattering and modeling the aggregated smart meter data from pro- & consumers (via DSO and IOE platform).
Our solution shares day ahead energy generation, consumption and balance among community members with a simple and user-friendly software.
We anonymise data and shares in the IOE platform.
The objectives of the CLEF project are to develop energy management system for self-consumption, operate at the community level and analyse the impacts.
1 | Energy consumption modeling and patterning
- Using smart meter data of consumers throughout Fluvius and IOEnergy platform.
- Deployment of big data analytics and ML tools for the energy consumption data-modeling.
2 | PV production forecasting
- Building predictive algorithms for energy generation with applying irradiation and weather forecast data.
3 | Information/communication platform and reanalysis
- Providing an application which gives prediction on PV production and energy consumption day ahead.
- Reanalysis data and evaluating performance of (pro-) consumers regarding energy usage as well as the quality of the prediction.
- Providing individual information and comparative report at a flexible time frame.
4 | Benchmarking for market validation analysis and hypothesis
- What key factors are related to the market and actor involvement influencing the effectiveness and success of smart community? Which type of information would trigger the practical actions towards adapting the behaviour from consumers side? How to keep consumers engage over a long period?
- How accurate does the forecasted generation/consumption data need to be? What time frame and resolution are required to transfer and process data in order to get the reliable and accurate prediction.
- What would be the impact of consumer’s behaviour adaption on the grid stress (in terms of peaks and voltage)?