A review of data-driven building energy consumption prediction studies

K Amasyali, NM El-Gohary - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy is the lifeblood of modern societies. In the past decades, the world's energy
consumption and associated CO 2 emissions increased rapidly due to the increases in …

Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC …

A Afram, F Janabi-Sharifi, AS Fung, K Raahemifar - Energy and Buildings, 2017 - Elsevier
In this paper, a comprehensive review of the artificial neural network (ANN) based model
predictive control (MPC) system design is carried out followed by a case study in which ANN …

Forecasting energy use in buildings using artificial neural networks: A review

J Runge, R Zmeureanu - Energies, 2019 - mdpi.com
During the past century, energy consumption and associated greenhouse gas emissions
have increased drastically due to a wide variety of factors including both technological and …

Review of modeling methods for HVAC systems

A Afram, F Janabi-Sharifi - Applied thermal engineering, 2014 - Elsevier
This work presents the literature review of the methods used to model the heating,
ventilation, and air conditioning (HVAC) systems. The model development is necessary for …

Short-term load forecasting in a non-residential building contrasting models and attributes

J Massana, C Pous, L Burgas, J Melendez… - Energy and …, 2015 - Elsevier
The electric grid is evolving. Smart grids and demand response systems will increase the
performance of the grid in terms of cost efficiency, resilience and safety. Accurate load …

Data-driven methods for building control—A review and promising future directions

ET Maddalena, Y Lian, CN Jones - Control Engineering Practice, 2020 - Elsevier
A review of the heating, ventilation and air-conditioning control problem for buildings is
presented with particular emphasis on its distinguishing features. Next, we not only examine …

Short-term non-residential load forecasting based on multiple sequences LSTM recurrent neural network

R Jiao, T Zhang, Y Jiang, H He - IEEE Access, 2018 - ieeexplore.ieee.org
The energy consumption by non-residential consumers in China accounts for a significant
proportion of the total energy consumption in the society. Thus, accurate non-residential …

Neural network model ensembles for building-level electricity load forecasts

JG Jetcheva, M Majidpour, WP Chen - Energy and Buildings, 2014 - Elsevier
The future power grid is expected to provide unprecedented flexibility in how energy is
generated, distributed, and managed, which increasingly necessitates an ability to perform …

[HTML][HTML] Towards cross-commodity energy-sharing communities–A review of the market, regulatory, and technical situation

S Paiho, J Kiljander, R Sarala, H Siikavirta… - … and Sustainable Energy …, 2021 - Elsevier
Meeting the energy goals of the European Union requires new ways of managing energy.
Decentralized energy management, cross-commodity energy production and usage …

Intelligent homes' technologies to optimize the energy performance for the net zero energy home

F AlFaris, A Juaidi, F Manzano-Agugliaro - Energy and Buildings, 2017 - Elsevier
Nowadays the concept of intelligent homes and smart technologies are spreading out and
becoming a vital strategy to optimize the energy performance. Specially that the internet is …