Data-driven prediction and optimization toward net-zero and positive-energy buildings: A systematic review
SN Mousavi, MG Villarreal-Marroquín… - Building and …, 2023 - Elsevier
Recent advances toward sustainable cities have promoted the concept of near-zero energy
consumption. A Positive Energy Building (PEB) model has been developed by the European …
consumption. A Positive Energy Building (PEB) model has been developed by the European …
[HTML][HTML] A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment
The occupants' presence, activities, and behaviour can significantly impact the building's
performance and energy efficiency. Currently, heating, ventilation, and air-conditioning …
performance and energy efficiency. Currently, heating, ventilation, and air-conditioning …
[HTML][HTML] Advances of machine learning in multi-energy district communities‒mechanisms, applications and perspectives
Y Zhou - Energy and AI, 2022 - Elsevier
Energy paradigm transition towards the carbon neutrality requires combined and continuous
efforts in cleaner power production, advanced energy storages, flexible district energy …
efforts in cleaner power production, advanced energy storages, flexible district energy …
Buildings' energy consumption prediction models based on buildings' characteristics: Research trends, taxonomy, and performance measures
Building's energy consumption prediction is essential to achieve energy efficiency and
sustain-ability. Building's energy consumption is highly dependent on buildings' …
sustain-ability. Building's energy consumption is highly dependent on buildings' …
Load forecasting with machine learning and deep learning methods
Characterizing the electric energy curve can improve the energy efficiency of existing
buildings without any structural change and is the basis for controlling and optimizing …
buildings without any structural change and is the basis for controlling and optimizing …
Federated learning with hyperparameter-based clustering for electrical load forecasting
Electrical load prediction has become an integral part of power system operation. Deep
learning models have found popularity for this purpose. However, to achieve a desired …
learning models have found popularity for this purpose. However, to achieve a desired …
Explaining household electricity consumption using quantile regression, decision tree and artificial neural network
Electricity as an energy carrier par excellence has a vital role in economic development.
However, even with the transformation of power systems that follows technological …
However, even with the transformation of power systems that follows technological …
A review on deep sequential models for forecasting time series data
Deep sequential (DS) models are extensively employed for forecasting time series data
since the dawn of the deep learning era, and they provide forecasts for the values required …
since the dawn of the deep learning era, and they provide forecasts for the values required …
Benchmarking energy consumption in universities: a review
TC Quevedo, MS Geraldi, AP Melo… - Journal of Building …, 2024 - Elsevier
Universities have an important role towards a sustainable future. It is essential to understand
their energy consumption and how to improve their efficiency. This paper aims to review the …
their energy consumption and how to improve their efficiency. This paper aims to review the …
Performance evaluation of the impact of clustering methods and parameters on adaptive neuro-fuzzy inference system models for electricity consumption prediction …
Increasing economic and population growth has led to a rise in electricity consumption.
Consequently, electrical utility firms must have a proper energy management strategy in …
Consequently, electrical utility firms must have a proper energy management strategy in …