Automating Building Energy Performance Simulation with EnergyPlus Using Modular JSON–Python Workflows: A Case Study of the Hilton Watford Hotel

Abstract

Accurate prediction of building energy performance is critical for achieving sustainability goals and reducing operational costs. This study presents a novel and automated simulation framework that integrates EnergyPlus 25.1 with modular JSON configurations and Python 3.11 scripting to streamline the modelling and analysis process. Using the Hilton Watford Hotel in the UK as a case study, the framework generates detailed Input Data Files (IDFs) based on architectural and operational data, enabling efficient exploration of various usage scenarios through batch simulations. Automation is achieved using custom Python scripts built on the Eppy library, allowing scalable modification and generation of simulation inputs. Post-processing and visualisation are performed using Pandas 2.0.3, NumPy 1.25.2, and Matplotlib 3.7.2, while model outputs are calibrated against measured performance data in accordance with ASHRAE guidelines. To enhance predictive capabilities, machine learning algorithms—Random Forest and XGBoost—are applied to estimate annual energy consumption under different operating conditions. This integrated approach not only reduces manual modelling effort but also narrows the gap between predicted and actual performance, offering a replicable pathway for retrofitting analysis and energy policy support in similar commercial buildings.