Heat Pump Detection from Household Electricity Consumption Using Different Machine Learning Classifiers
Name
John-William Mukose
Abstract
Heat pumps are becoming more popular for heating houses as they use less energy than traditional heating methods, offsetting their higher installation costs. They have the added advantage of causing less pollution, due to less energy consumption and due to the ability to use electricity generated from renewable sources.
This thesis aims to determine whether certain premises have heat pumps installed or not, based on their hourly electricity consumption. This is a time series classification task i.e. the hourly electricity consumption of each household in the dataset is a time series, and a classifier is to be trained on this data to be able to classify a household as having a heat pump installed or not. Different machine learning models are used: Recurrent and Convolutional Neural Networks, as well as Logistic Regression.The latter serves as a baseline to compare deep neural models against simple, yet interpretable logistic regression models. The ground truth data as to whether a premise has a heat pump or not is obtained from Eesti Energia heat pump sales records. We face an additional challenge of training the machine learning models with a small dataset of only 113 premises with heat pumps.
We found that 2D Convolutional Neural Network with time series data reshaped
into a 2 Dimensional image is the optimum classifier for our data. Our thesis presents an innovative solution of using CNN on heat pump time-series data instead of using sequential models of Long Short Term Memory (LSTM) networks, which are normally the main model used for time series data. In this case, the CNN has the advantage over LSTM of faster training times as well as better accuracy. The results come with the caveat that better and more reliable results can be obtained if a larger dataset becomes available.
This thesis aims to determine whether certain premises have heat pumps installed or not, based on their hourly electricity consumption. This is a time series classification task i.e. the hourly electricity consumption of each household in the dataset is a time series, and a classifier is to be trained on this data to be able to classify a household as having a heat pump installed or not. Different machine learning models are used: Recurrent and Convolutional Neural Networks, as well as Logistic Regression.The latter serves as a baseline to compare deep neural models against simple, yet interpretable logistic regression models. The ground truth data as to whether a premise has a heat pump or not is obtained from Eesti Energia heat pump sales records. We face an additional challenge of training the machine learning models with a small dataset of only 113 premises with heat pumps.
We found that 2D Convolutional Neural Network with time series data reshaped
into a 2 Dimensional image is the optimum classifier for our data. Our thesis presents an innovative solution of using CNN on heat pump time-series data instead of using sequential models of Long Short Term Memory (LSTM) networks, which are normally the main model used for time series data. In this case, the CNN has the advantage over LSTM of faster training times as well as better accuracy. The results come with the caveat that better and more reliable results can be obtained if a larger dataset becomes available.
Graduation Thesis language
English
Graduation Thesis type
Master - Data Science
Supervisor(s)
Kallol Roy, Kristjan Eljand
Defence year
2022