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 …

Urban energy modeling is essential in planning electricity generation and efficiently
managing electric power systems. Various urban energy models were developed for several …

Electricity price forecasting is a key aspect for market participants to maximize their
economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non …

This paper focuses on the development of shallow and deep neural networks in the form of
multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short …

Heat loads change dynamically with meteorological conditions and user demand, and the
related accurate prediction algorithms are conducive to the realization of optimized …

Buildings account for over a third of end energy demand in many countries worldwide.
Modelling this demand accurately marks the first step in producing forecasts that can help …

Peak load demand forecasting is important in building unit sectors, as climate change,
technological development, and energy policies are causing an increase in peak demand …

Several studies have indicated a potential to exploit the thermal inertia of individual
residential buildings for demand response purposes using model predictive control and time …

Deep neural networks are proposed for short-term natural gas load forecasting. Deep
learning has proven to be a powerful tool for many classification problems seeing significant …

Accurate prediction of the return temperature is critical to energy efficiency of the district
heating system (DHS). The support vector machines (SVMs) and artificial neural networks …