Development of electricity consumption forecasting model for campus-scaled building using machine learning

Abstract

The demand for electricity in buildings on a national and international scale is currently rising rapidly. Building electricity usage can be decreased by using a forecasting model. It can reduce utility costs not just for one building but also throughout a whole region. According to literature review, machine-learning and deep-learning techniques have been used in previous studies on forecasting electricity consumption. However, there is a dearth of research into the use of clustering to predict electricity consumption in tropical regions such as Thailand or any of the countries in Southeast Asia. In this project, we present new research for hourly forecasting building energy usage. 1-hour interval electricity consumption data is collected from nineteen buildings for a year and five months by smart meters. 1-hour interval weather data including PM 10, PM 2.5, temperature, and humidity collected is also collected from one building. The analysis of the correlation between weather data and electricity consumption indicated that that there was a weak correlation between weather and electricity consumption data. Vector Auto Regression (VAR), Vector Auto- Regressive Moving Average (VARMA), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models were used to develop the forecasting models as the baseline models. The SVR model can outperform the other models with the lowest RMSE validation scores on training dataset. The hyperparameters of SVR models were optimized to maximize forecasting accuracy on training dataset. To reduce time consuming for training and optimizing the models, the k-Shape clustering approach is used to classify electricity consumption into pattern groups and to use the centroid of each cluster as a representation of the cluster's electricity consumption data in order to forecast the electricity consumption of buildings within the cluster. The result of comparing the forecasting performance of SVR