Buildings are in charge of more than 40% of power consumption demand and greenhouse gas emissions
Need for a better understanding, efficient characterization and prediction of the energy building performance at different scales for informed decision-making to reduce energy costs and consumption
Multi-source data (sensors, smart meters, numerical simulations, weather data…)
What's the Objective ?
Better understand and monitor the energy performance of buildings at different scales in order to help stakeholders make better decisions in terms of energy
Building sector and real-estate markets, Distribution system operators, Public authorities, Policymakers, …
What's at stake ?
- Need for a transition towards a low carbon economy to limit the effect of climate change
- Not only increase the use of renewable energies
- Also a more efficient use of the energy that we have avoiding waste
- Better managing the energy of our buildings using AI goes exactly in this direction
- Building energy modelling & forecasting is a key tool to inform decision-making for energy planning, building design/renovation, establishment of energy performance contract, predictive maintenance, advice to occupants, energy policies definition, …
What are the challenges ?
- The cost of equipping and monitoring buildings with appropriate sensors and meters is not negligible
- Historical data are difficult to obtain (monitoring equipment relatively recent)
- Numerical simulations used to model energy consumption are also costly and are not always sufficiently accurate
-> Obtaining sufficient and accurate data can be challenging
What are the possible solutions ?
- Times series forecasting
- Fault Detection and Diagnosis
- Transfer learning is a promising technique to develop accurate and reliable building energy prediction models.
- Use the knowledge about energy performance learned from a set of information-rich buildings to buildings with limited data
- Reduce the computational time and cost, while also improving the accuracy of predictions
- Pay attention to the explainability of the provided predictions to make the latter more trusted by end-users