Predictive Analysis of Heating Systems for Fault Detection

Abstract: Background : The heat load has an emergent role in the energy consumption of the heating system in buildings. The industry experts also have been constantly focusing on the heat load optimization techniques and in the recent years, numerous Machine Learning (ML) techniques have come into picture to resolve various tasks. Objectives : This study is mainly focused on to analyze the time-series hourly data and choose suitable Supervised Machine Learning approach among Multivariate Linear Regression (MLR), Support Vector Regression, and Multi-layer Perceptron (MLP) Regressor so as to predict heat demand for identifying the deviating behaviors and potentially faults. Methods : An experiment is performed and the method consists of imputing the missing values, extreme values and selection of six different feature sets. Cross validation on Multivariate Linear Regression, Support Vector Regression, and Multi-layer Perceptron Regressor was performed to find the best suitable algorithm. Finally the residuals of the best algorithm and the best feature set was used to find the fault using the calculation of studentized residuals. Because of the time-series based data in data set, regression based algorithms was the best suitable choice to work with such type of data that is continuous. The faults in the system were identified based on the studentized residuals that exceeds the threshold value of 3 are classified as fault. Results : Among the regression based algorithms, Multi-layer Perceptron Regressor resulted in Mean Absolute Error (MAE) of 1.77 and Mean Absolute Percentage Error (MAPE) 0.29% on the feature set 1. Multivariate Linear Regression shown Mean Absolute Error 1.83 and Mean Absolute Percentage Error 0.31% on feature set 1 that has relatively higher error for the metrics of Mean Absolute Error and Mean Absolute Percentage Error as comparing to Multi-layer Perceptron Regressor. Support Vector Regression (SVR) shown Mean Absolute Error 2.54 that is higher than that of both Multivariate Linear Regression and Multi-layer Perceptron Regressor, while theMean Absolute Percentage Error 0.24% that is similar to Multivariate Linear Regression and Multi-layer Perceptron Regressor on the feature set 1. So the best performing algorithm is Multi-layer Perceptron Regressor. The feature sets 4,5 and 6 which are super-sets of 1, 2 and 3 feature sets along with addition of outdoor temperature. These feature sets 4, 5 and 6 did not show much impact even after considering the outdoor temperature. From, the Table 5.1 the feature sets 1, 2 and 3 are comparitively better than feature sets 4, 5 and 6 for the metrics Mean Absolute Error and Mean Absolute Percentage Error.Finally on comparing the first three feature sets, the feature set 1 resulted in less error for all three algorithms as comparing to feature set 2 and feature set 3 that can be seen in Table 5.1. So the feature set 1 is the best feature set. Conclusions : Multi-layer Perceptron Regressor perfomed well on six different feature sets comparing with Multivariate Linear Regression and Support Vector Regression. The feature set 1 had shown Mean Absolute Error and Mean Absolute Percentage values relatively low than other feature sets. Therefore the feature set 1 was the best performing and the best suited algorithm was Multi-layer Perceptron Regressor. The Figure A.3 represents the flow of work done in the thesis.