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 …
[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 …
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) …
Accurate detection of electricity theft using classification algorithms and Internet of Things in smart grid
Electricity theft is one of the most significant factors among non-technical losses. Because of
electricity theft, genuine users have to pay more, supply quality decreases, and generation …
electricity theft, genuine users have to pay more, supply quality decreases, and generation …
Advanced ml-based ensemble and deep learning models for short-term load forecasting: Comparative analysis using feature engineering
PP Phyo, C Jeenanunta - Applied Sciences, 2022 - mdpi.com
Short-term load forecasting (STLF) plays a pivotal role in the electricity industry because it
helps reduce, generate, and operate costs by balancing supply and demand. Recently, the …
helps reduce, generate, and operate costs by balancing supply and demand. Recently, the …