PSO-Stacking improved ensemble model for campus building energy consumption forecasting based on priority feature selection

Journal of Building Engineering, Год журнала: 2023, Номер 72, С. 106589 - 106589

Опубликована: Апрель 20, 2023

Язык: Английский

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Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review

Data, Год журнала: 2024, Номер 9(1), С. 13 - 13

Опубликована: Янв. 11, 2024

This paper addresses the challenges in forecasting electrical energy current era of renewable integration. It reviews advanced adaptive methodologies while also analyzing evolution research this field through bibliometric analysis. The review highlights key contributions and limitations models with an emphasis on traditional methods. analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, deep learning have potential to model dynamic nature consumption, but they higher computational demands data requirements. aims offer a balanced view advancements methods, guiding researchers, policymakers, industry experts. advocates for collaborative innovation enhance accuracy support development resilient, sustainable systems.

Язык: Английский

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New formulation for predicting total dissolved gas supersaturation in dam reservoir: application of hybrid artificial intelligence models based on multiple signal decomposition

Artificial Intelligence Review, Год журнала: 2024, Номер 57(4)

Опубликована: Март 9, 2024

Abstract Total dissolved gas (TDG) concentration plays an important role in the control of aquatic life. Elevated TDG can cause gas-bubble trauma fish (GBT). Therefore, controlling fluctuation has become great importance for different disciplines surface water environmental engineering.. Nowadays, direct estimation is expensive and time-consuming. Hence, this work proposes a new modelling framework predicting based on integration machine learning (ML) models multiresolution signal decomposition. The proposed ML were trained validated using hourly data obtained from four stations at United States Geological Survey. dataset are composed from: ( i ) temperature T w ), ii barometric pressure BP iii discharge Q which used as input variables prediction. strategy conducted two steps. First, six singles model namely: multilayer perceptron neural network, Gaussian process regression, random forest iv vector functional link, v adaptive boosting, vi Bootstrap aggregating (Bagging), developed , their performances compared. Second, was introduced combination empirical mode decomposition (EMD), variational (VMD), wavelet transform (EWT) preprocessing algorithms with building hybrid models. signals decomposed to extract intrinsic functions (IMFs) by EMD VMD methods analysis (MRA) components EWT method. Then after, IMFs MRA selected regraded integral part thereof. single prediction compared several statistical metrics namely, root mean square error, absolute coefficient determination R 2 Nash–Sutcliffe efficiency (NSE). times high number repetitions, depending kind modeling process. results gave good agreement between predicted situ measured dataset. Overall, Bagging performed better than other five NSE values 0.906 0.902, respectively. However, extracted EMD, have contributed improvement models’ performances, significantly increased reaching 0.996 0.995. Experimental showed superiority more importantly improving predictive accuracy TDG. Graphical abstract

Язык: Английский

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Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load

Energy, Год журнала: 2024, Номер 302, С. 131814 - 131814

Опубликована: Май 26, 2024

Язык: Английский

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AI Technologies and Their Applications in Small-Scale Electric Power Systems

IEEE Access, Год журнала: 2024, Номер 12, С. 109984 - 110001

Опубликована: Янв. 1, 2024

As the landscape of electric power systems is transforming towards decentralization, small-scale have garnered increased attention. Meanwhile, proliferation artificial intelligence (AI) technologies has provided new opportunities for system management. Thus, this review paper examines AI technology applications and their range uses in electrical systems. First, a brief overview evolution importance integration given. The background section explains principles systems, including stand-alone grid-interactive microgrids, hybrid virtual plants. A thorough analysis conducted on effects aspects such as energy consumption, demand response, grid management, operation, generation, storage. Based foundation, Acceleration Performance Indicators (AAPIs) are developed to establish standardized framework evaluating comparing different studies. AAPI considers binary scoring five quantitative Key (KPIs) qualitative KPIs examined through three-tiered scale – established, evolved, emerging.

Язык: Английский

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Modeling heat capacity of liquid siloxanes using artificial intelligence methods

Fluid Phase Equilibria, Год журнала: 2025, Номер unknown, С. 114423 - 114423

Опубликована: Март 1, 2025

Язык: Английский

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Online decoupling feature framework for optimal probabilistic load forecasting in concept drift environments

Applied Energy, Год журнала: 2025, Номер 392, С. 125952 - 125952

Опубликована: Апрель 25, 2025

Язык: Английский

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Multi-task learning and single-task learning joint multi-energy load forecasting of integrated energy systems considering meteorological variations

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 128269 - 128269

Опубликована: Май 1, 2025

Язык: Английский

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Studying the Thermodynamic Phase Stability of Organic–Inorganic Hybrid Perovskites Using Machine Learning

Molecules, Год журнала: 2024, Номер 29(13), С. 2974 - 2974

Опубликована: Июнь 22, 2024

As an important photovoltaic material, organic–inorganic hybrid perovskites have attracted much attention in the field of solar cells, but their instability is one main challenges limiting commercial application. However, search for stable among thousands perovskite materials still faces great challenges. In this work, energy above convex hull values was predicted based on four different machine learning algorithms, namely random forest regression (RFR), support vector (SVR), XGBoost regression, and LightGBM to study thermodynamic phase stability perovskites. The results show that algorithm has a low prediction error can effectively capture key features related Meanwhile, Shapley Additive Explanation (SHAP) method used analyze algorithm. third ionization B element most critical feature stability, second electron affinity ions at X site, which are significantly negatively correlated with (Ehull). screening high site worthy priority. help us understand correlation between features, assist rapid discovery highly materials.

Язык: Английский

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Analysis and prediction of the changes in groundwater resources under heavy precipitation and ecological water replenishment

Journal of Water and Climate Change, Год журнала: 2023, Номер 14(6), С. 1762 - 1778

Опубликована: Май 22, 2023

Abstract Identifying the influence of heavy precipitation and ecological water replenishment (EWR) on groundwater resources is essential for management risk prevention. This study innovatively developed a resource analysis prediction model integrated with level fluctuation method, correlation analysis, machine learning method under EWR. Water results showed that compared January 1, 2021, area increased 4.46 × 108 m3 August 28. Compared small flow EWR, was main contributor to rise in level. Correlation found elevation, specific yield, permeability coefficient show positive correlations recharge. Machine among models 35 monitoring wells, extreme gradient boosting (XGB) random forest (RF) performed best 30 wells five respectively. The increase storage predicted deviated from actual value by only 0.6 107 (prediction bias 1.3%), indicating performance good condition. can help better understand change trend conditions

Язык: Английский

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