Optimization and prediction in the early design stage of office buildings using genetic and XGBoost algorithms

Building and Environment, Journal Year: 2022, Volume and Issue: 218, P. 109081 - 109081

Published: April 14, 2022

Language: Английский

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A comprehensive review on deep learning approaches for short-term load forecasting

Renewable and Sustainable Energy Reviews, Journal Year: 2023, Volume and Issue: 189, P. 114031 - 114031

Published: Nov. 9, 2023

Language: Английский

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Optimization and prediction of energy consumption, light and thermal comfort in teaching building atriums using NSGA-II and machine learning

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 86, P. 108687 - 108687

Published: Jan. 30, 2024

Language: Английский

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An Improved Neural Network Based on SENet for Sleep Stage Classification

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2022, Volume and Issue: 26(10), P. 4948 - 4956

Published: March 8, 2022

Sleep staging is an important step in analyzing sleep quality. Traditional manual analysis by psychologists time-consuming. In this paper, we propose automatic model with improved attention module and hidden Markov (HMM). The driven single-channel electroencephalogram (EEG) data. It automatically extracts features through two convolution kernels different scales. Subsequently, based on Squeeze-and-Excitation Networks (SENet) will perform feature fusion. neural network give a preliminary stage the learned features. Finally, HMM apply transition rules to refine classification. proposed method tested sleep-EDFx dataset achieves excellent performance. accuracy Fpz-Cz channel 84.6%, kappa coefficient 0.79. For Pz-Oz channel, 82.3% 0.76. experimental results show that mechanism plays positive role And our improves classification addition, applying helps improve performance, especially N1, which difficult identify.

Language: Английский

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A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting

Applied Energy, Journal Year: 2023, Volume and Issue: 348, P. 121563 - 121563

Published: July 15, 2023

Language: Английский

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Accurate and efficient daily carbon emission forecasting based on improved ARIMA

Applied Energy, Journal Year: 2024, Volume and Issue: 376, P. 124232 - 124232

Published: Aug. 22, 2024

Language: Английский

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Evaluation of energy consumption data for business consumers

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Language: Английский

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Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine

Energies, Journal Year: 2022, Volume and Issue: 15(13), P. 4569 - 4569

Published: June 22, 2022

The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many which have direct implications for humanity’s survival. forecast mine site use is one low-cost approaches conservation. Accurate predictions do indeed assist us better understanding source high and aid making early decisions by setting expectations. Machine Learning (ML) methods are known to be best approach achieving desired results prediction tasks this area. As result, machine learning been used several research involving operational residential buildings. Only few research, however, investigated feasibility algorithms predicting open-pit mines. To close gap, work provides an application RapidMiner tool time series using real-time data obtained from smart grid placed experimental mine. This study compares performance four daily consumption: Artificial Neural Network (ANN), Support Vector (SVM), Decision Tree (DT), Random Forest (RF). models were trained, tested, then evaluated. In order assess models’ metrics study, namely correlation (R), mean absolute error (MAE), root squared (RMSE), relative (RRSE). reveals RF most effective predictive model forecasting similar cases.

Language: Английский

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A Solar Irradiance Forecasting Framework Based on the CEE-WGAN-LSTM Model

Sensors, Journal Year: 2023, Volume and Issue: 23(5), P. 2799 - 2799

Published: March 3, 2023

With the rapid development of solar energy plants in recent years, accurate prediction power generation has become an important and challenging problem modern intelligent grid systems. To improve forecasting accuracy generation, effective robust decomposition-integration method for two-channel irradiance is proposed this study, which uses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a Wasserstein generative adversarial network (WGAN), long short-term memory (LSTM). The consists three essential stages. First, output signal divided into several relatively simple subsequences using CEEMDAN method, noticeable frequency differences. Second, high low-frequency are predicted WGAN LSTM models, respectively. Last, values each component integrated to obtain final results. developed model data technology, together advanced machine learning (ML) deep (DL) models identify appropriate dependencies topology. experiments show that compared many traditional methods can produce results under different evaluation criteria. Compared suboptimal model, MAEs, MAPEs, RMSEs four seasons decreased by 3.51%, 6.11%, 2.25%,

Language: Английский

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Distributed intelligence for IoT-based smart cities: a survey

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(27), P. 16621 - 16656

Published: July 22, 2024

Language: Английский

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