MACHINE LEARNING PREDICTION OF MATERIAL BEHAVIOR A multiple linear regression model of the observed behaviour was developed as a complement to the mechanistic model in order to determine near-term predictions of PCM behaviour and rankings of features that influence these predictions. The training data were drawn from the weather station recordings of outside temperature and radiation, interior sensors’ temperature readings, and readings from 11 sensors embedded in a single panel. At each timestep, feature vectors were created for the complete set of readings, and prediction vectors were created for panel sensor reading, both in a range of intervals. The model was trained using the Multiple Linear Regression model of the SciKit learn python library using three days of data and subsequently used to predict running predictions over the following seven days, for which empirical data were also available. In this manner, the accuracy of the predictions could be assessed relative to recordings that were outside of the original training sets. VERIFICATION OF RESULTS The verification of the mechanistic model was conducted through qualitative visual inspection and quantitative numerical analysis of time-series predictions, both of which utilised the simulation interface. For visual inspection, images of simulation steps were compared with concurrent time-lapse images of the panels in situ to confirm that the melting patterns bore a resemblance to predicted behaviour. The mechanistic model was largely successful at predicting the qualitative melting behaviour, which began where the PCM thickness was minimal and proceeded gradually to the thicker regions where more enthalpy was required to melt the material (Figure 34). The multiple linear regression model produced accurate forecasts of system behaviour based on three days of training data. As was expected, the 5-minute forecast yielded the most accurate predictions (standard deviation for the 11 sensors between 0.15°C and 0.23°C), while the 20- and 60-minute forecasts yielded slightly larger deviations (between 0.29°C and 0.47°C and between 0.47°C and 0.76°C). What is notable about the deviations in the longerrange forecasts is that they occur largely in the liquid state, where the aforementioned solar effects complicate analysis. Conversely, these models proved extremely effective at predicting the onset and duration of melting and solidification, suggesting that they are well-suited to forecasting the visual effects of phase change, which are the primary focus of this study.
CASE STUDIES OF REGENERATIVE DESIGN
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