INTELLIGENT MACHINE LEARNING FRAMEWORK FOR ENERGY CONSUMPTION PREDICTION IN SMART BUILDINGS: A COMPARATIVE STATISTICAL AND PREDICTIVE DIAGNOSTIC ANALYSIS
TL;DR
A detailed machine learning model to forecast energy usage in an IoT-monitored educational facility in Jaipur, India, during one operation regimes, namely mechanical cooling (AC-ON), serves to legitimize ensemble machine learning as a sound premise toward the smart building environment in terms of defining intelligent energy optimization and demand-responsive control strategies.
Abstract
The increasing global energy needs especially in the building sector, which contributes major percentage of the total global energy use, requires smart and data-driven solutions to effective energy management. Heating, Ventilation, and Air-Conditioning (HVAC) systems represent major energy consumption of all building, and thus the precise energy consumption forecast is an essential requirement of the sustainable use of smart buildings. In the current study, a detailed machine learning (ML) model to forecast energy usage in an IoT-monitored educational facility in Jaipur, India, during one operation regimes, namely mechanical cooling (AC-ON) is provided. Distributed IoT sensors were systematically used to gather a real-world dataset of 365 daily observations to measure multivariate thermal, solar and envelope heat transfer, ventilation and energy parameters. Mechanical ventilation coupled with infiltration and dry-bulb temperature outdoors proved to be the main source of cooling energy changeability. Six regression algorithms were comparatively analyzed: Multiple Linear Regression, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Classification and Regression Tree (CART), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost). Mean Absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R 2) were used to evaluate the model performance. Ensemble models were significantly improved predictors; Gradient Boosting had the minimum prediction error in cooling energy (MAE: 6.420, R 2: 0.990) and equipment energy consumption (MAE: 0.2716, R 2: 0.9997). The analysis of the scatter plot revealed prediction diagnosis which indicated close clustering of the residues around the reference line which was ideal, which proved the model dependability at the extremes of the seasons. The results serve to legitimize ensemble machine learning as a sound premise toward the smart building environment in terms of defining intelligent energy optimization and demand-responsive control strategies.