Abstract
Deep energy renovation of building stock came more into focus in the European Union due to energy efficiency related directives. Many buildings that must undergo deep energy renovation are old and may lack design/renovation documentation, or possible degradation of materials might have occurred in building elements over time. Thermal transmittance (i.e. U-value) is one of the most important parameters for determining the transmission heat losses through building envelope elements. It depends on the thickness and thermal properties of all the materials that form a building element. In-situ U-value can be determined by ISO 9869-1 standard (Heat Flux Method - HFM). Still, measurement duration is one of the reasons why HFM is not widely used in field testing before the renovation design process commences. This paper analyzes the possibility of reducing the measurement time by conducting parallel measurements with one heat-flux sensor. This parallelization could be achieved by applying a specific class of the Artificial Neural Network (ANN) on HFM results to predict unknown heat flux based on collected interior and exterior air temperatures. After the satisfying prediction is achieved, HFM sensor can be relocated to another measuring location. Paper shows a comparison of four ANN cases applied to HFM results for a measurement held on one multi-layer wall – multilayer perceptron with three neurons in one hidden layer, long short-term memory with 100 units, gated recurrent unit with 100 units and combination of 50 long short-term memory units and 50 gated recurrent units. The analysis gave promising results in term of predicting the heat flux rate based on the two input temperatures. Additional analysis on another wall showed possible limitations of the method that serves as a direction for further research on this topic.
Highlights
The building sector is recognized as an area where action can be taken through energy efficiency regulations to reduce humanity’s impact on the environment [1], it is clear that there is a significant need for energy retrofitting of the building stock [2]
Paper shows a comparison of four Artificial Neural Network (ANN) cases applied to heat flux method (HFM) results for a measurement held on one multi-layer wall – multilayer perceptron with three neurons in one hidden layer, long short-term memory with 100 units, gated recurrent unit with 100 units and combination of 50 long short-term memory units and 50 gated recurrent units
If we observe training/validation ratio 1/4, the best matching is registered for MLP3 ANN architecture with root mean squared error (RMSE) 2.02 which can be seen in table 1
Summary
The building sector is recognized as an area where action can be taken through energy efficiency regulations to reduce humanity’s impact on the environment [1], it is clear that there is a significant need for energy retrofitting of the building stock [2]. Since the boundary conditions are difficult to handle, as the external boundary condition is the outside air temperature and the internal one is the constant room temperature [14], the test time of several different building elements can be reduced either by using multiple HFM sensors (which is financially and operationally inefficient [18]) or by evaluating the sensor results according to the assumed behavior pattern. Such prediction of sensor results is potentially possible with artificial intelligence [19]
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