Solar Irradiance Prediction using meteorological data by ensemble models

Kamble, Megha; Ghosh, Sudeshna; Patel, Poojan

doi:10.1109/idea49133.2020.9170678

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…The forecasting strategy procedure is illustrated in figures 4(a), (b). In the proposed swarm-based ensemble forecasting strategy, search space is based on the ranges of these three hyperparameters given in equation (12) Search S S S 12 space lr u b ( ) = ´The lower and upper boundaries for all hyperparameters are given in table 2. PSO systematically explores the possible hyperparameter value combinations in n-dimensional search space and finds the best optimal position by minimizing the loss.…”

Section: Optimized Lstm Bilstm and Gru Using Psomentioning

confidence: 99%

“…To forecast solar power, the existing studies have significant use of persistence [6][7][8], physical [9,10], hybrid/ensemble [11,12] , statistical, machine learning (ML) [13][14][15], and deep learning (DL) techniques [16]. The efficacy of support vector machine (SVM), artificial neural network (ANN), and extreme learning machine (ELM) algorithms are compared to forecast daily sun irradiance [17].…”

Section: Introductionmentioning

confidence: 99%

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Short-term solar photovoltaic power forecasting using ensemble forecasting strategy for renewable resources based power systems

Kanwal,

Hayat,

Tayab

2024

Eng. Res. Express

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Environmentally friendly renewable energy sources have been developed and commercialized to mitigate the impact of climate change on the environment. Solar photovoltaic (PV) systems have gained much attention as a power generation source for various uses, including the primary utility grid power supply. There has been a significant increase in both on-grid and off-grid solar PV installations. Because of the highly unpredictable nature of solar power generation, it is crucial to accurately forecast solar power for renewable resources-based power systems. In this research, a swarm-based ensemble forecasting strategy has been proposed to predict solar PV power by combining three strategies, i.e., particle swarm optimization-based gated recurrent unit (PSO-GRU), PSO-based long short-term memory (PSO-LSTM), and PSO-based bidirectional long short-term memory (PSO-BiLSTM). Bayesian model averaging (BMA) combines the output of the proposed strategy by aggregating the output of each swarm-based approach. The performance of the suggested approach is evaluated and verified using historical data of solar PV power acquired from Griffith University, Australia. Python 3.11 is used to validate the performance of the proposed ensemble strategy and compare it with several competing strategies. The proposed ensemble strategy outperforms other comparative strategies in terms of root mean square error (RMSE), normalized RMSE (NRMSE), and mean absolute error (MAE).

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Section: Optimized Lstm Bilstm and Gru Using Psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-term solar photovoltaic power forecasting using ensemble forecasting strategy for renewable resources based power systems

Kanwal,

Hayat,

Tayab

2024

Eng. Res. Express

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“…For predicting the SIr, a publicly available dataset (provided by NASA and available at https://www.kaggle.com/dronio/SolarEnergy, accessed on 26 October 2020) is used in this work. Before this study, these data have been used for validating the performance of different developed models [151,152]. The SIr plays the role of the target parameter to be predicted with the inputs of temperature (T), barometric pressure (BP), humidity (H), wind direction (WD), and wind speed (WS).…”

Section: Data Provisionmentioning

confidence: 99%

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Moayedi

Mosavi

2021

Energies

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Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

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“…For predicting the SIr, a publicly available dataset (provided by NASA and available on https://www.kaggle.com/dronio/SolarEnergy) is used in this work. Prior to this study, this data has been used for validating the performance of different developed models [116,117]. Considering R2 values calculated in Figure 3 (0.5402, 0.0142, 0.0512, 0.053, and 0.0054 for the T, BP, H, WD, and WS, respectively), it can be said that the most meaningful relationship (among these five inputs) is obtained for the T. In a general view, the values of SIr tend to increase with the increase in this factor.…”

Section: Data Provisionmentioning

confidence: 99%

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

Moayedi¹,

Mosavi²

2021

Preprint

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Proper management of solar energy, as an effective renewable source, is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO) is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., NPop, R_rate, Ps_rate, P_field, and N_field) are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

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Solar Irradiance Prediction using meteorological data by ensemble models

Cited by 3 publications

References 12 publications

Short-term solar photovoltaic power forecasting using ensemble forecasting strategy for renewable resources based power systems

Short-term solar photovoltaic power forecasting using ensemble forecasting strategy for renewable resources based power systems

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

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