Digital x-ray-based performance characterization Frequent energy meter data opens up for new inductive or data-driven tools. The tools act as a bit like a kind of x-ray vi- sion through the layers of the individual walls. Given this x-ray- based knowledge of the performance of the individual walls, the tool can provide evidence-based information about the performance of the separate buildings. This is useful, for in- stance, before deciding on a possible energy renovation. Similar AI tools and digital twin models can be used to obtain a better control of the indoor climate. This has been demon- strated, e.g., in the social housing Taastrupgaard in Høje-Taas- trup Municipality, where digital tools have been used to show that many radiators were misused. Sensors for the indoor CO2 levels, temperature, and humidity have also been installed in schools, e.g., in Hørsholm and Rudersdal, to monitor the indoor climate and obtain a better comfort and learning environment in the schools using the platform from Climify. Climify has also developed a FeedMe app, which can ensure individual and op- timized control of heating and ventilation of the classrooms, lowering the return temperature, minimizing mold risk, and obtaining energy savings. Forecasting Load forecasting obviously calls for data-driven tools. Lately, new methods for coherent forecasting of the heating load on all relevant time horizons from, say, 15 minutes to 96 hours ahead have proven to give considerable (15 - 30 pct) improve- ments in the accuracy of load forecasts at some of the largest DH operators in Denmark. These forecasting improvements lead to significant economic benefits in temperature control, production planning, and participation in the electricity mar- kets.
ods). Obviously, such data can be used to identify if the build- ing calls, e.g., for a renovation like replacing the windows.
Energy performance characterization of buildings Traditionally, energy performance characterization and labe- ling have mostly been based on deductive analysis, i.e., based on assumed theory for energy transfer and material proper- ties. Today the existence of frequent meter readings and, e.g., nearby meteorological observations data opens up for evi- dence-based inductive analysis, i.e., data-driven methods. The deductive approach used for buildings today Today the energy performance characterization and energy la- beling of buildings are based on rather simple calculations and a visit by an energy consultant. The cost of getting such a label is relatively high, around 700-1000 Euros. The methods used today are often criticized. The main problem is that two buildings, which in theory should be identical, might have a somewhat different energy performances in practice. This well-known performance gap between predicted and ac- tual building energy performance can be significant. Even after correcting for differences in user behavior and occupancy, the actual energy consumption can easily be 50-100 pct higher than the theoretical consumption. Generally, the technical sources for discrepancies between the theoretical performance and the measured performance can be broken into three baskets: The design and simulation phase (limitations, inaccuracies, and assumptions in the theory used to predict the performance); the construction and com- missioning phase (caused by the poor quality of workmanship and differences between assumed and actual materials, com- ponents and systems); and, the operation phase (poor-func- tioning of the systems and in particular the HVAC system).
Forecasting of PV and thermal solar energy production also calls for data-driven approaches for several reasons. By using
Simulation-Based vs Data-Driven Temperature Optimization Simulation-Based vs Data-Driven Temperature Optimazation
Simulation-based TO
Data-driven TO
Approach
Deductive (simulation/theoretical values)
Inductive (data-driven, self learning)
•
•
Optimal usage
• Simulation of new operational scenarios (where no data exists)
• Control of temperature and flow, reduction of heat loss, real time data
Temperature profile
• Temperature calculated using theoretical values for pipes, insulation, soil, etc.
• Temperature estimated using real life data and statistical/AI-based learning
-
Does NOT take into account: • Dirtiness, •
+ Take into account: • Dirtiness, •
Distribution net
Soil properties (temperature, humidity, ...)
Soil properties (temperature, humidity, …)
Leakage,
•
Leakage,
•
Wet or damaged insulation,
•
Wet or damaged insulation,
•
Deviations from design values / drawings
•
Deviations from design values / drawings
•
Characteristics
-
Constant parameters •
+ Self calibrating / automated learning •
Require recalibration, which can be difficult and time-consuming
Automatic recalibration for instance due to new costumers, heavy rainfall, damaged insulation, etc.
Production facilities
• New production facilities call for recalibration
• New production facilities call for recalibration
Figure 1: Differences between simulation-based and data-driven temperature optimization
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