A review of intelligent systems for the prediction of wind energy using machine learning
Renewable energies are playing a key role in the energy transition into efficient and
sustainable power generation. The global wind energy development has growth in recent …
sustainable power generation. The global wind energy development has growth in recent …
Data-driven energy consumption prediction of a university office building using machine learning algorithms
Redundant consumption of energy in buildings is an important issue that causes increasing
problems of climate change and global warming in the world. Therefore, it is necessary to …
problems of climate change and global warming in the world. Therefore, it is necessary to …
[HTML][HTML] Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach
Stakeholders such as urban planners and energy policymakers use building energy
performance modeling and analysis to develop strategic sustainable energy plans with the …
performance modeling and analysis to develop strategic sustainable energy plans with the …
A data-driven energy performance gap prediction model using machine learning
The energy performance gap is a significant obstacle to the realization of ambitions to
mitigate the environmental impact of buildings. Although extensive research has been …
mitigate the environmental impact of buildings. Although extensive research has been …
[HTML][HTML] SCADA system dataset exploration and machine learning based forecast for wind turbines
Effective short-term wind power forecast is essential for adequate power system stability,
dispatching and cost control. There are various significant renewable energy sources …
dispatching and cost control. There are various significant renewable energy sources …
Improvement of mechanical properties and water resistance of bio-based thermal insulation material via silane treatment
Buildings, whether commercial or residential, consume a huge proportion of the energy
produced globally to maintain livable conditions within their walls. It is estimated that 40% of …
produced globally to maintain livable conditions within their walls. It is estimated that 40% of …
Prophet-EEMD-LSTM based method for predicting energy consumption in the paint workshop
Y Lu, B Sheng, G Fu, R Luo, G Chen, Y Huang - Applied Soft Computing, 2023 - Elsevier
Energy conservation and preventive maintenance of equipment require the ability to
accurately predict future trends in shop floor power consumption to keep track of equipment …
accurately predict future trends in shop floor power consumption to keep track of equipment …
[PDF][PDF] Leveraging machine learning to optimize renewable energy integration in develo** economies
The integration of renewable energy sources into power grids is a critical challenge for
develo** economies, where infrastructure limitations, unpredictable energy demand, and …
develo** economies, where infrastructure limitations, unpredictable energy demand, and …
Deep ensemble-based approach using randomized low-rank approximation for sustainable groundwater level prediction
Groundwater is the most abundant freshwater resource. Agriculture, industrialization, and
domestic water supplies rely on it. The depletion of groundwater leads to drought …
domestic water supplies rely on it. The depletion of groundwater leads to drought …
Machine learning method based on symbiotic organism search algorithm for thermal load prediction in buildings
This research investigates the efficacy of a proposed novel machine learning tool for the
optimal simulation of building thermal load. By applying a symbiotic organism search (SOS) …
optimal simulation of building thermal load. By applying a symbiotic organism search (SOS) …