Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks

Hassan, Muhammed A.; Bailek, Nadjem; Bouchouicha, Kada; Nwokolo, Samuel Chukwujindu

doi:10.1016/j.renene.2021.02.103

Cited by 87 publications

(39 citation statements)

References 62 publications

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“…Figure 7 shows MSE versus the number of iterations (epochs) for the three cases of training (blue line), validation (green line) and testing (red line). The limited dataset results in a validation error of the order of 10 −2 , which is larger than the errors shown by NAR in other applications [22][23][24][25][26]. However, we can consider it acceptable since the error on the real measurement is one order of magnitude higher (∼10 −1 Celsius degree).…”

Section: Results With Nar Neural Networkmentioning

confidence: 88%

Novel Model Based on Artificial Neural Networks to Predict Short-Term Temperature Evolution in Museum Environment

Bile

Tari

Grinde

et al. 2022

Sensors

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The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural Heritage area, with the aim of predicting short-term microclimatic values based on data collected at Rosenborg Castle (Copenhagen), housing the Royal Danish Collection. Specifically, this study applied the NAR (Nonlinear Autoregressive) and NARX (Nonlinear Autoregressive with Exogenous) models to the Rosenborg microclimate time series. Even if the two models were applied to small datasets, they have shown a good adaptive capacity predicting short-time future values. This work explores the use of AI in very short forecasting of microclimate variables in museums as a potential tool for decision-support systems to limit the climate-induced damages of artworks within the scope of their preventive conservation. The proposed model could be a useful support tool for the management of the museums.

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Section: Results With Nar Neural Networkmentioning

confidence: 88%

Novel Model Based on Artificial Neural Networks to Predict Short-Term Temperature Evolution in Museum Environment

Bile

Tari

Grinde

et al. 2022

Sensors

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“…An improved particle swarm optimization (PSO) method is used in kernel-based extreme learning machines to develop an online PV power prediction model [24]. Muhammed et al [25] proposed a nonlinear autoregressive recurrent neural network optimized by a genetic algorithm for ultra-short-term PV power prediction. Ma et al developed a short-term prediction method of PV power based on the modified firefly algorithm (MFA)-optimized Elman neural network [26] to solve the problem of the randomness of initial weights and thresholds of the Elman NN.…”

Section: Introductionmentioning

confidence: 99%

PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type

Qiu

Sun

et al. 2022

Energies

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Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.

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“…Photovoltaic power forecasting methods are generally divided into physical methods and statistical methods. Physical methods are not suitable in many cases because of their low prediction accuracy and high calculation cost (Ma et al, 2014;Yao, 2014;Hassan et al, 2021). The statistical method optimizes the mapping relationship between historical samples and actual photovoltaic power by minimizing the error, which is proved to be effective in the field of solar energy prediction.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Power Prediction of a Photovoltaic Power Station Based on the VMD-CEEMDAN-LSTM Model

Wang

Liu²,

Guan³

2022

Front. Energy Res.

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The prediction of photovoltaic power generation is helpful to the overall allocation of power planning departments and improves the utilization rate of photovoltaic power generation. Therefore, this study puts forward an ultra-short-term power forecasting model of a photovoltaic power station based on modal decomposition and deep learning. The methodology involved taking the data of a 50 MW photovoltaic power generation system in the Inner Mongolia Autonomous Region as a sample. Furthermore, the weather conditions were classified, and the historical power data were decomposed into multiple VMF subcomponents and residual terms by the VMD method. Then, the residual term was decomposed twice by the CEEMDAN method. All subcomponents were sent to the LSTM network for prediction, and the predicted value of the photovoltaic power station was obtained by superimposing the subcomponent prediction results. ARIMA, SVM, LSTM, and VMD-LSTM models were built to compare the accuracy with the proposed models. The results revealed that the prediction accuracy of a non-combination forecasting model was limited when the weather suddenly changed. The VMD method was used to decompose the residual term twice, which could fully extract the complex data information in the residual term, and when compared with the VMD-LSTM model, the eRMSE, eMAPE, and eTIC of the VMD-CEEMDAN-LSTM model were reduced by 0.104, 16.596, and 0.038, respectively. The second decomposition technology has obvious prediction advantages. The proposed quadratic modal decomposition model effectively improves the precision of ultra-short-term prediction of photovoltaic power plants.

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Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks

Cited by 87 publications

References 62 publications

Novel Model Based on Artificial Neural Networks to Predict Short-Term Temperature Evolution in Museum Environment

Novel Model Based on Artificial Neural Networks to Predict Short-Term Temperature Evolution in Museum Environment

PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type

Ultra-Short-Term Power Prediction of a Photovoltaic Power Station Based on the VMD-CEEMDAN-LSTM Model

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