Localized assembly in biological activity: Origin of life and future of nanoarchitectonics

Advances in Colloid and Interface Science, Journal Year: 2025, Volume and Issue: 339, P. 103420 - 103420

Published: Feb. 3, 2025

Language: Английский

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Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

Applied Energy, Journal Year: 2024, Volume and Issue: 371, P. 123652 - 123652

Published: June 12, 2024

Language: Английский

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Enhanced Insights into Effluent Prediction in Wastewater Treatment Plants: Comprehensive Deep Learning Model Explanation Based on SHAP

ACS ES&T Water, Journal Year: 2024, Volume and Issue: 4(4), P. 1904 - 1915

Published: April 2, 2024

Models are increasingly being utilized to improve the understanding and operation of wastewater treatment plants (WWTPs) in face escalating water resource challenges. Abundant operational data provide extensive opportunities for development machine learning (ML) deep (DL) models. However, coupling time lag among features exacerbate black-box nature such models, hindering their application WWTPs. In this study, we construct a DL model using long short-term memory (LSTM) algorithm capable accurately predicting effluent quality full-scale WWTP with finely tuned hyperparameters rationally chosen input features. Comprehensive explanation based on Shapley additive explanations (SHAP) is implemented clarify contributions multivariate series (MTS) inputs predicted results terms feature dimensions. The LSTM models exhibit excellent accuracy (R2 0.96, 0.95, 0.76 MAPE 5.49, 7.17, 13.37%, respectively) chemical oxygen demand (COD), total phosphorus (TP), nitrogen (TN) better than other baseline ML SHAP quantify what most important when they exert influence how impact results. analysis from temporal dimension further explains characteristics process justifies introduction MTS. Compared correlation without engineering, selection method by significantly enhances predictive accuracy. combinations adjusted values, strong interactions significant output identified. This novel attempt both explainability MTS prediction work shows potential applying WWTPs performance.

Language: Английский

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Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications

Applied Energy, Journal Year: 2024, Volume and Issue: 372, P. 123753 - 123753

Published: June 25, 2024

Language: Английский

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A multiscale network with mixed features and extended regional weather forecasts for predicting short-term photovoltaic power

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134792 - 134792

Published: Jan. 1, 2025

Language: Английский

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Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100958 - 100958

Published: March 1, 2025

Language: Английский

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Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation

Energies, Journal Year: 2024, Volume and Issue: 17(14), P. 3435 - 3435

Published: July 12, 2024

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient management systems prediction technologies. Through optimizing scheduling integration PV generation, stability reliability of can be further improved. In this study, a new model is introduced that combines strengths convolutional neural networks (CNNs), long short-term memory (LSTM) networks, attention mechanisms, so we call algorithm CNN-LSTM-Attention (CLA). addition, Crested Porcupine Optimizer (CPO) utilized solve problem generation. This abbreviated as CPO-CLA. first time CPO has been into LSTM for parameter optimization. effectively capture univariate multivariate series patterns, multiple relevant target variables patterns (MRTPPs) are employed CPO-CLA model. The results show superior traditional methods recent popular models terms accuracy stability, especially 13 h timestep. mechanisms enables adaptively focus most historical data future prediction. optimizes network parameters, which ensures robust generalization ability great significance establishing trust market. Ultimately, it will help integrate renewable reliably efficiently.

Language: Английский

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Self-learning dynamic graph neural network with self-attention based on historical data and future data for multi-task multivariate residential air conditioning forecasting

Applied Energy, Journal Year: 2024, Volume and Issue: 364, P. 123156 - 123156

Published: April 6, 2024

Language: Английский

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Forecasting building energy demand and on-site power generation for residential buildings using long and short-term memory method with transfer learning

Applied Energy, Journal Year: 2024, Volume and Issue: 368, P. 123500 - 123500

Published: May 23, 2024

Language: Английский

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NSGA-II based short-term building energy management using optimal LSTM-MLP forecasts

International Journal of Electrical Power & Energy Systems, Journal Year: 2024, Volume and Issue: 159, P. 110070 - 110070

Published: June 3, 2024

To conduct analysis on the field of electricity management in buildings is crucial to contribute clean energy promotion, efficiency, and resilience against climate change. This manuscript proposes a methodology for modeling predictive calibrated system (EMS) using hybrid that combines long short-term memory multilayer perceptron models (LSTM-MLP) optimized by non-dominated sorting genetic algorithm II (NSGA-II). The proposed approach utilizes global forecast (GFS) data anticipate consumption fluctuations optimize use distributed sources, such as photovoltaic (PV) production, based knowledge prices free market one day ahead. trade-off building conducted with NSGA-II, guaranteeing exploration exploitation while minimizing costs wastes. research carried out demonstrates effectiveness LSTM-MLP model advantages NSGA-II hyperparameter tuning balance sustainable practices. tested an existing building, Industrial Engineering School located Campus Lagoas-Marcosende Universidade de Vigo, Spain.

Language: Английский

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