Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants

Energy, Journal Year: 2023, Volume and Issue: 274, P. 127350 - 127350

Published: March 30, 2023

Language: Английский

---

A short-term wind power forecasting method based on multivariate signal decomposition and variable selection

Applied Energy, Journal Year: 2024, Volume and Issue: 360, P. 122759 - 122759

Published: Feb. 6, 2024

Language: Английский

---

Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S

Energy, Journal Year: 2024, Volume and Issue: 305, P. 132228 - 132228

Published: Oct. 1, 2024

Language: Английский

---

Wind power forecasting system with data enhancement and algorithm improvement

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 196, P. 114349 - 114349

Published: March 1, 2024

Language: Английский

---

A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 241, P. 122487 - 122487

Published: Nov. 17, 2023

Language: Английский

---

Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables

Applied Energy, Journal Year: 2023, Volume and Issue: 353, P. 122155 - 122155

Published: Oct. 27, 2023

Language: Английский

---

Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 148, P. 110864 - 110864

Published: Sept. 26, 2023

Language: Английский

---

Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model

Applied Energy, Journal Year: 2024, Volume and Issue: 359, P. 122624 - 122624

Published: Jan. 24, 2024

Wind energy is an environment friendly, low-carbon, and cost-effective renewable source. It is, however, difficult to integrate wind into a mixed grid due its high volatility intermittency. For conversion systems be reliable efficient, accurate speed (WS) forecasting fundamental. This study cascades convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) in order obtain model for hourly WS by utilizing several meteorological variables as inputs their effects on predicted WS. input selection, the mutation grey wolf optimizer (TMGWO) used. efficient optimization of CBiLSTM hyperparameters, hybrid Bayesian Optimization HyperBand (BOHB) algorithm The combined usage TMGWO, BOHB, leads three-phase (i.e., 3P-CBiLSTM). performance 3P-CBiLSTM benchmarked against standalone BiLSTMs, LSTMs, gradient boosting (GBRs), random forest (RFRs), decision tree regressors (DTRs). statistical analysis forecasted reveals that highly effective over other benchmark methods. objective also registers highest percentage errors (≈ 53.4 – 81.8%) within smallest error range ≤ |0.25| ms−1 amongst all tested sites. Despite remarkable results achieved, cannot generally understood, so eXplainable Artificial Intelligence (xAI) technique was used explaining local global outputs, based Local Interpretable Model-Agnostic Explanations (LIME) SHapley Additive exPlanations (SHAP). Both xAI methods determined antecedent most significant predictor forecasting. Therefore, we aver proposed can employed help farm operators making quality decisions maximizing power integration reduced

Language: Английский

---

A robust chaos-inspired artificial intelligence model for dealing with nonlinear dynamics in wind speed forecasting

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2393 - e2393

Published: Oct. 10, 2024

The global impacts of climate change have become increasingly pronounced in recent years due to the rise greenhouse gas emissions from fossil fuels. This trend threatens water resources, ecological balance, and could lead desertification drought. To address these challenges, reducing fuel consumption embracing renewable energy sources is crucial. Among these, wind stands out as a clean source garnering more attention each day. However, variable unpredictable nature speed presents challenge integrating into electricity grid. Accurate forecasting essential overcome obstacles optimize usage. study focuses on developing robust model capable handling non-linear dynamics minimize losses improve efficiency. Wind data Bandırma meteorological station Marmara region Turkey, known for its potential, was decomposed intrinsic mode functions (IMFs) using empirical decomposition (REMD). extracted IMFs were then fed long short-term memory (LSTM) architecture whose parameters estimated African vultures optimization (AVO) algorithm based tent chaotic mapping. approach aimed build highly accurate model. performance proposed improving compared with that particle swarm (CPSO) algorithm. Finally, highlights potential utilizing advanced techniques deep learning models forecasting, ultimately contributing efficient sustainable generation. hybrid represents significant step forward research practical applications.

Language: Английский

---

Innovative framework for accurate and transparent forecasting of energy consumption: A fusion of feature selection and interpretable machine learning

Applied Energy, Journal Year: 2024, Volume and Issue: 366, P. 123314 - 123314

Published: April 29, 2024

The study presents a novel framework integrating feature selection (FS) and machine learning (ML) techniques to forecast inland national energy consumption (EC) in the United Kingdom across all sources. This innovative strategically combines three FS approaches with five interpretable ML models using Shapley Additive Explanations (SHAP), dual goal of enhancing accuracy transparency EC predictions. By meticulously selecting most pertinent features from diverse features—including meteorological conditions, socioeconomic parameters, historical patterns different primary fuels—the proposed enhances robustness forecasting model. is achieved through benchmarking approaches: ensemble filter, wrapper, hybrid filter-wrapper. In addition, we introduce filter FS, synthesizing outcomes multiple base methods make well-informed decisions about retention. Experimental results underscore efficacy both wrapper filter-wrapper models, ensuring process remains comprehensible while utilizing manageable number (four eight). experimental indicate that subsets are usually selected for each combined approach not only demonstrates framework's capability provide accurate forecasts but also establishes it as valuable tool policymakers analysts.

Language: Английский

---