Buildings account for a significant portion of our overall energy usage and associated
greenhouse gas emissions. With the increasing concerns regarding climate change, there …

In this article, a systematic literature review of 419 articles on energy demand modeling,
published between 2015 and 2020, is presented. This provides researchers with an …

Environmental pollutants in the air have long been a great threat to the health and life of
human society and the volume of these pollutants is rapidly increasing. Human beings …

The development of energy-efficient buildings by considering early-stage design parameters
can help reduce buildings' energy consumption. Machine learning tools are getting popular …

Buildings are among the largest energy consumers in the world. As new technologies have
been developed, great advances have been made in buildings, turning conventional …

Urbanization increases electricity demand due to population growth and economic activity.
To meet consumer's demands at all times, it is necessary to predict the future building …

The energy demand in the building sector is anticipated to increase with climate change and
the high energy consumption is responsible for releasing enormous amounts of CO 2 into …

Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure
reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time …

Electrical load forecasting is crucial to achieving better efficiency, reliability, and power
quality in modern power systems. Applying short‐term load forecasting, a balance can be …

Artificial intelligence (AI) constitutes a kind of modelling method widely used in various fields
of science including energy and environmental engineering [1]. Moreover, AI is considered a …