Comparison of artificial intelligence methods in estimation of daily global solar radiation

Khosravi, Ali; Nunes, Raphael; Assad, Mamdouh El Haj; Machado, Luiz

doi:10.1016/j.jclepro.2018.05.147

Cited by 103 publications

(36 citation statements)

References 52 publications

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“…The proposed methodology employs meta-heuristic algorithms for ANFIS training and combines MLPNN, ANFIS, and meta-heuristics to increase the forecasting accuracy. Some research related to training ANFIS using meta-heuristics is given in [17][18][19][20].…”

Section: Contributionmentioning

confidence: 99%

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

et al. 2019

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Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.

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Section: Contributionmentioning

confidence: 99%

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

et al. 2019

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“…Knowledge of the meteorological data of the site where the technology is implemented is essential for the proper functioning of the Dish/Stirling system [6]. There are different methods of predicting or estimating the daily global radiation from one region to another [7,8].…”

Section: Introductionmentioning

confidence: 99%

Simplified Modeling and Simulation of Electricity Production from a Dish/Stirling System

Babikir

Chara-Dackou

Njomo

et al. 2020

International Journal of Photoenergy

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This work is part of the dynamic of proposing a solution to the problem of access to electricity in Chad, which has a rate of access to electricity of 3%. N’Djamena has significant solar potential that can be harnessed to generate electricity. In this paper, we present a theoretical study of the performances of the Dish/Stirling system with the purpose of producing electricity, based on a mathematical model taking into account each of the subparts of the system (concentrator, solar cavity receiver, and Stirling engine). Hydrogen is preferred to helium as the working fluid for operating the Stirling engine at high temperatures. This coupled model made it possible to estimate the monthly average of the electric power produced by this modular system and also its overall solar electricity yield.

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“…More and more scholars have applied it to the prediction of solar activity index. Huang et al . applied support vector regression (SVR) for 1–3 days forecast of F10.7.…”

Section: Introductionmentioning

confidence: 99%

“…More and more scholars have applied it to the prediction of solar activity index. [10][11][12][13] Huang et al [14] applied support vector regression (SVR) for 1-3 days forecast of F10.7. The results showed that SVR model outmatched the conventional multilayer feedforward neural network.…”

Section: Introductionmentioning

confidence: 99%

The 10.7‐cm radio flux multistep forecasting based on empirical mode decomposition and back propagation neural network

Luo

Zhu

Jiang

et al. 2020

IEEJ Transactions Elec Engng

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The 10.7‐cm solar radio flux (F10.7) is among the most widely used indices of solar activity, whose prediction plays a vital role in the field of meteorology and aerospace. In this paper, a novel approach for a specific amalgamation of back propagation neural network (BPNN) and empirical mode decomposition (EMD) is proposed, which is called EMD‐BP, aimed at forecasting the daily F10.7 values 1–27 days ahead. The daily F10.7 values, which are highly nonlinear and unstable time series, are transformed into a series of subsignals with different frequency components by EMD and trained by the BPNN. For estimating the performance of the description EMD‐BP, the support vector regression (SVR), Long Short‐Term Memory neural network and BPNN as the reference models are analyzed in different conditions. The results indicate that the proposed model improves the F10.7 forecasting within 27 days, compared with the common machine learning algorithms and transport model. Furthermore, a tremendous decline of forecast error is determined in high‐level solar activity, in contrast with SVR and BPNN. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Comparison of artificial intelligence methods in estimation of daily global solar radiation

Cited by 103 publications

References 52 publications

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

Simplified Modeling and Simulation of Electricity Production from a Dish/Stirling System

The 10.7‐cm radio flux multistep forecasting based on empirical mode decomposition and back propagation neural network

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