Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions
The rapid growth of urban populations worldwide imposes new challenges on citizens' daily
lives, including environmental pollution, public security, road congestion, etc. New …
lives, including environmental pollution, public security, road congestion, etc. New …
Energy and sustainable development in smart cities: An overview
Smart cities are an innovative concept for managing metropolitan areas to increase their
residents' sustainability and quality of life. This article examines the management and …
residents' sustainability and quality of life. This article examines the management and …
A novel CNN-GRU-based hybrid approach for short-term residential load forecasting
Electric energy forecasting domain attracts researchers due to its key role in saving energy
resources, where mainstream existing models are based on Gradient Boosting Regression …
resources, where mainstream existing models are based on Gradient Boosting Regression …
Long short-term memory network-based metaheuristic for effective electric energy consumption prediction
The Electric Energy Consumption Prediction (EECP) is a complex and important process in
an intelligent energy management system and its importance has been increasing rapidly …
an intelligent energy management system and its importance has been increasing rapidly …
Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China
M Wang, W Wang, L Wu - Energy, 2022 - Elsevier
Scientific prediction of regional energy is of practical significance for rational control of
energy supply. In this paper, to further minimize the influence of subjective factors, the grey …
energy supply. In this paper, to further minimize the influence of subjective factors, the grey …
Dual stream network with attention mechanism for photovoltaic power forecasting
The operations of renewable power generation systems highly depend on precise
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting
Due to global warming and climate changes, buildings including residential and commercial
are significant contributors to energy consumption. To this end, net zero energy building …
are significant contributors to energy consumption. To this end, net zero energy building …
A novel convolutional neural network-based approach for fault classification in photovoltaic arrays
Fault diagnosis in photovoltaic (PV) arrays is essential in enhancing power output as well as
the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high …
the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high …
Efficient short-term electricity load forecasting for effective energy management
Short-term electrical energy load forecasting is one of the most significant problems
associated with energy management for smart grids, which aims to optimize the operational …
associated with energy management for smart grids, which aims to optimize the operational …
[HTML][HTML] Privacy-preserving federated learning for residential short-term load forecasting
With high levels of intermittent power generation and dynamic demand patterns, accurate
forecasts for residential loads have become essential. Smart meters can play an important …
forecasts for residential loads have become essential. Smart meters can play an important …