Bayesian optimisation algorithm based optimised deep bidirectional long short term memory for global horizontal irradiance prediction in long-term horizon DOI Creative Commons
Manoharan Madhiarasan

Frontiers in Energy Research, Journal Year: 2025, Volume and Issue: 13

Published: Feb. 19, 2025

With the continued development and progress of industrialisation, modernisation, smart cities, global energy demand continues to increase. Photovoltaic systems are used control CO 2 emissions manage demand. (PV) system public utility, effective planning, control, operation compels accurate Global Horizontal Irradiance (GHI) prediction. This paper is ardent about designing a novel hybrid GHI prediction method: Bayesian Optimisation algorithm-based Optimized Deep Bidirectional Long Short Term Memory (BOA-D-BiLSTM). work attempts fine-tune hyperparameters employing optimisation. Globally ranked fifth in solar photovoltaic deployment, INDIA Two Region Solar Dataset from NOAA-National Oceanic Atmospheric Administration was assess proposed BOA-D-BiLSTM approach for long-term horizon. The superior performance highlighted with help experimental results comparative analysis grid search random search. Furthermore, forecasting effectiveness compared other models, namely, Persistence Model, ARIMA, BPN, RNN, SVR, Boosted Tree, LSTM, BiLSTM. Compared models according resulting evaluation error metrics, suggested model has minor Root Mean Squared Error: 0.0026 0.0030, Absolute Error:0.0016 0.0018, Mean-Squared 6.6852 × 10 −06 8.8628 R-squared: 0.9994 0.9988 on both dataset 1 respectively. outperforms baseline models. Thus, viable potential distributed generation planning control.

Language: Английский

Enhancing solar irradiance prediction for sustainable energy solutions employing a hybrid machine learning model; improving hydrogen production through Photoelectrochemical device DOI

Yandi Zhang

Applied Energy, Journal Year: 2025, Volume and Issue: 382, P. 125280 - 125280

Published: Jan. 13, 2025

Language: Английский

Citations

1
Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm DOI
Vahid Tavakol Aghaei,

Arda Ağababaoğlu,

Biram Bawo

et al.

Applied Energy, Journal Year: 2023, Volume and Issue: 341, P. 121108 - 121108

Published: April 21, 2023

Language: Английский

Citations

18
Advancing solar energy forecasting with modified ANN and light GBM learning algorithms DOI Creative Commons
Muhammad Farhan Hanif,

Muhammad Sabir Naveed,

Mohamed Metwaly

et al.

AIMS energy, Journal Year: 2024, Volume and Issue: 12(2), P. 350 - 386

Published: Jan. 1, 2024

<abstract> <p>In the evolving field of solar energy, precise forecasting Solar Irradiance (SI) stands as a pivotal challenge for optimization photovoltaic (PV) systems. Addressing inadequacies in current techniques, we introduced advanced machine learning models, namely Rectified Linear Unit Activation with Adaptive Moment Estimation Neural Network (RELAD-ANN) and Support Vector Machine Individual Parameter Features (LSIPF). These models broke new ground by striking an unprecedented balance between computational efficiency predictive accuracy, specifically engineered to overcome common pitfalls such overfitting data inconsistency. The RELAD-ANN model, its multi-layer architecture, sets standard detecting nuanced dynamics SI meteorological variables. By integrating sophisticated regression methods like Regression (SVR) Lightweight Gradient Boosting Machines (Light GBM), our results illuminated intricate relationship influencing factors, marking novel contribution domain energy forecasting. With R<sup>2</sup> 0.935, MAE 8.20, MAPE 3.48%, model outshone other signifying potential accurate reliable forecasting, when compared existing Multi-Layer Perceptron, Long Short-Term Memory (LSTM), Multilayer-LSTM, Gated Recurrent Unit, 1-dimensional Convolutional Network, while LSIPF showed limitations ability. Light GBM emerged robust approach evaluating environmental influences on SI, outperforming SVR model. Our findings contributed significantly systems could be applied globally, offering promising direction renewable management real-time forecasting.</p> </abstract>

Language: Английский

Citations

8
Performance prediction of a ground source heat pump system using denoised long short-term memory neural network optimised by fast non-dominated sorting genetic algorithm-II DOI
Chaoran Wang,

Yu Xiong,

Chanjuan Han

et al.

Geothermics, Journal Year: 2024, Volume and Issue: 120, P. 103002 - 103002

Published: March 22, 2024

Language: Английский

Citations

8
Hour-Ahead Photovoltaic Power Prediction Combining BiLSTM and Bayesian Optimization Algorithm, with Bootstrap Resampling for Interval Predictions DOI Creative Commons
Reinier Herrera Casanova, Arturo Conde Enrı́quez, Carlos Santos

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(3), P. 882 - 882

Published: Jan. 29, 2024

Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic one hour ahead. The dataset under examination originates from small PV installation located at Polytechnic School University Alcala. To improve quality historical data and optimize performance, robust preprocessing algorithm is implemented. BiLSTM synergistically combined with Bayesian optimization (BOA) to fine-tune its primary hyperparameters, thereby enhancing predictive efficacy. performance proposed evaluated across diverse meteorological seasonal conditions. In deterministic forecasting, findings indicate superiority over alternative models employed in this research domain, specifically multilayer perceptron (MLP) neural network random forest (RF) ensemble model. Compared MLP RF reference models, achieves reductions normalized mean absolute error (nMAE) 75.03% 77.01%, respectively, demonstrating effectiveness type prediction. Moreover, interval utilizing bootstrap resampling method conducted, acquired intervals carefully adjusted meet desired confidence levels, robustness flexibility predictions.

Language: Английский

Citations

6
An intelligent decision support system for warranty claims forecasting: Merits of social media and quality function deployment DOI
Ali Nikseresht, Sajjad Shokouhyar‎, Erfan Babaee Tırkolaee

et al.

Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 201, P. 123268 - 123268

Published: Feb. 15, 2024

Language: Английский

Citations

6
A solar irradiance forecasting model using iterative filtering and bidirectional long short-term memory DOI
Pardeep Singla, Sumit Saroha, Manoj Duhan

et al.

Energy Sources Part A Recovery Utilization and Environmental Effects, Journal Year: 2024, Volume and Issue: 46(1), P. 8202 - 8222

Published: June 26, 2024

Language: Английский

Citations

6
Seasonal solar irradiance forecasting using artificial intelligence techniques with uncertainty analysis DOI Creative Commons

V. Gayathry,

K. Deepa, Surender Reddy Salkuti

et al.

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

Published: Aug. 2, 2024

Renewable integration in utility grid is crucial the current energy scenario. Optimized utilization of renewable can minimize consumption from grid. This demands accurate forecasting contribution and planning. Most researches aim to find a suitable model terms accuracy error metrics. However, uncertainty variability these forecasts are also significant. work combines point forecast with interval provide comprehensive information about uncertainty. In this work, solar irradiance carried out using artificial intelligence (AI) techniques. Forecasting done seasonal auto-regressive moving average exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques performance evaluated. SVR exhibited best R

Language: Английский

Citations

6
Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm DOI
Guolian Hou, Lingling Ye, Ting Huang

et al.

Energy, Journal Year: 2024, Volume and Issue: 308, P. 132879 - 132879

Published: Aug. 23, 2024

Language: Английский

Citations

6
A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique DOI

Lihong Qiu,

Wentao Ma,

Xiaoyang Feng

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 359, P. 122719 - 122719

Published: Jan. 31, 2024

Language: Английский

Citations

5