A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 27, 2024

Language: Английский

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Calibration study of uncertainty parameters for nearly-zero energy buildings based on a novel approximate Bayesian approach

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135823 - 135823

Published: March 1, 2025

Language: Английский

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Enhancing Smart Home Efficiency with Heuristic-Based Energy Optimization

Computers, Journal Year: 2025, Volume and Issue: 14(4), P. 149 - 149

Published: April 16, 2025

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient management an important factor. To address scheduling of appliances under Demand-Side Management, this article explores use heuristic-based optimization techniques (HOTs) homes (SHs) equipped renewable and sustainable resources (RSERs) storage systems (ESSs). The optimal model for minimization peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven (WDO), Bacterial Foraging (BFO) Modified (GmPSO) algorithm, to minimize electricity costs, PAR, carbon emissions delay discomfort. This research investigates results three real-world scenarios. scenarios demonstrate benefits gradually assembling RSERs ESSs integrating them into SHs employing HOTs. simulation show substantial outcomes, scenario Condition 1, GmPSO decreased from 300 kg 69.23 kg, reducing 76.9%; bill prices were also cut unplanned value 400.00 cents 150 cents, a 62.5% reduction. PAR was unscheduled 4.5 2.2 which reduced 51.1%. 2 showed that 0.5 (unscheduled) 0.2, 60% reduction; costs 500.00 200.00 250.00 reduction GmPSO. 3, where batteries integrated, algorithm emission 158.3 208.3 24%. cost 500 GmPSO, decreasing overall 40%. achieved 57.1% 2.8 1.2.

Language: Английский

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A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building

Applied Energy, Journal Year: 2024, Volume and Issue: 376, P. 124180 - 124180

Published: Aug. 20, 2024

Language: Английский

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Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction

Energies, Journal Year: 2024, Volume and Issue: 17(12), P. 3054 - 3054

Published: June 20, 2024

The prediction of heating and cooling loads using machine learning algorithms has been considered frequently in the research literature. However, many studies default values hyperparameters. This manuscript addresses both selection best regressor tuning hyperparameter a novel nature-inspired algorithm, namely, Multi-Objective Plum Tree Algorithm. two objectives that were optimized averages predictions. three compared Extra Trees Regressor, Gradient Boosting Random Forest Regressor sklearn Python library. We five hyperparameters which configurable for each regressors. solutions ranked MOORA method. Algorithm returned root mean square error value equal to 0.035719 0.076197. results are comparable ones obtained standard multi-objective such as Grey Wolf Optimizer, Particle Swarm Optimization, NSGA-II. also performant concerning previous studies, same experimental dataset.

Language: Английский

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Machine Learning Algorithms for Energy Consumption Prediction in Smart Homes: A Comparative Study

Lecture notes on data engineering and communications technologies, Journal Year: 2024, Volume and Issue: unknown, P. 75 - 94

Published: Dec. 11, 2024

Language: Английский

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