Due to the recent advancements in the Internet of Things and data science techniques, a
wide range of studies have investigated the use of data mining (DM) and machine learning …

Access to electricity is a cornerstone for sustainable development and is pivotal to a
country's progress. The absence of electricity impedes development and elevates poverty …

Abstract Machine learning models have been widely used to study the prediction of heating
and cooling loads in residential buildings. However, most of these methods use the default …

Paying attention to the feature engineering problems is the basis for constructing a more
accurate building energy consumption prediction model, which helps debug, control, and …

Accurate forecasting of cooling and heating loads is critical for optimizing the energy usage
of devices and planning for energy storage in building district energy systems (BDESs). Data …

Develo** energy-efficient buildings considering building design parameters can help
reduce buildings' energy consumption. The energy efficiency of residential buildings is …

Residential energy consumption data and related sociodemographic information are critical
for energy demand management, including providing personalized services, ensuring …

This study presents a new framework that integrates machine learning and a domain
knowledge-based expert system to improve building energy flexibility. In this framework, a …

This paper develops a two-stage multi-objective bi-level framework to optimize the sizing of
a grid-connected electricity-hydrogen system. Firstly, a multi-objective bi-level capacity …

Recently, deep reinforcement learning has emerged as a popular approach for enhancing
thermal energy management in buildings due to its flexibility and model-free nature …