Predicting Building Energy Consumption Using Gaussian Process Regression: Algorithms, Visualization and Web Applications

Abstract

Growing attention has been drawn to energy use forecasting for smart grid applications. There has been a surge of interest in applying Gaussian process (GP) modeling to predicting building energy use. As a continuation of my previous projects, this thesis develops a web application that allows users without programming skills to predict and visualize energy demand through Gaussian process regression. The web application implements both baseline prediction and next-day prediction. This study also explores the visualization techniques that facilitate the analysis of energy consumption patterns and transform the data into informative insights. The technologies used in the web application include Flask, Heroku and Highcharts.js. This thesis presents two case studies to demonstrate the use of the web application and discusses the prediction accuracy. One case study is to predict electric energy, chilled water and steam consumption of a campus building. The second case study is to predict next-day electric energy demand of a high-tech industry area.