A Two-stage Forecasting Approach for Hvac Systems: Comparative Analysis of Ml and Dl Models With Forecasted Temperature Inputs
Year: 2025
Type: article
Abstract: Heating, ventilation, and air-conditioning (HVAC) systems are among the most energy-intensive components in commercial buildings. Accurate forecasting of HVAC energy consumption is essential for implementing energy-efficient control strategies. This study provides a detailed comparative analysis of classical machine learning (ML) and deep learning (DL) models for forecasting HVAC energy consumption using both predicted and actual indoor temperature data. Leveraging an open-source, high-resolution dataset from the Oak Ridge National Laboratory's FRP-2 multizone building, we implement a two-stage framework. The first stage involves temperature prediction using relative humidity and airflow data, while the second stage forecasts HVAC energy consumption using either predicted or actual temperature inputs. We evaluate Linear Regression, Random Forest, Support Vector Machine, GRU, and LSTM models. The results reveal that while models with actual temperature inputs yield superior accuracy (R² up to 0.884), models using predicted temperatures still achieve competitive performance. Our findings suggest that ML and DL models, particularly LSTM, offer promising capabilities for real-time energy forecasting in smart building environments.
Author: Ganesh Murade
Institution: University of Engineering & Management, Jaipur
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414570530
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