Computationally expedient Photovoltaic power Forecasting: A LSTM ensemble method augmented with adaptive weighting and data segmentation technique

Ahmed, Razin; Sreeram, Victor; Togneri, Roberto; Datta, Amitava; Arif, Muammer Din

doi:10.1016/j.enconman.2022.115563

Cited by 49 publications

(13 citation statements)

References 95 publications

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“…The modern photovoltaic power generation forecasting methods start to use artificial intelligence algorithms such as support vector machines [12][13][14], random forests [15,16] and other machine learning algorithms [17], convolutional neural networks [18,19], long short-term memory artificial neural networks [20][21][22] and other deep learning algorithms [23].…”

Section: Modern Photovoltaic Power Generation Forecasting Methodsmentioning

confidence: 99%

“…A meta-learning strategy based on multiple loss function networks is proposed to train the two-deep networks to ensure the high robustness of the extracted convolutional features. Ahmed et al [21] used the integration-based long short-term memory (LSTM) algorithm, which consists of 10 LSTM models. The method compares the effects of seasonal and periodic variations on time series data and PV output forecasting.…”

Section: Modern Photovoltaic Power Generation Forecasting Methodsmentioning

confidence: 99%

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Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Fan¹,

Hui-zhen²,

Chen³

et al. 2022

Glob. J. Energ. Technol. Res. Updat.

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With the continuous expansion of the capacity of photovoltaic power generation systems, accurate power generation load forecasting can make grid dispatching more reasonable and optimize load distribution. This paper proposes a combined forecasting model based on Auto Regression Integrate Moving Average (ARIMA), back propagation neural network (BPNN), and support vector regression (SVR), namely ARIMA-BPNN-SVR model, aiming at the problem of low accuracy of a single model and traditional forecasting model. Through the complementary advantages of ARIMA, BPNN, and SVR models, the model has good anti-noise ability, nonlinear mapping, and adaptive ability when processing photovoltaic power generation data. Data experiments are carried out on solar photovoltaic power generation in the United States, and the accuracy of model forecasting is evaluated according to MAE, MSE, RMSE, and MAPE. The experimental results show that the proposed ARIMA-BPNN-SVR outperforms the forecasting performance of the single models ARIMA, BPNN, and SVR. Its MAE, MSE, RMSE and MAPE are 0.53, 0.41, 0.64 and 0.84 respectively. In the Wilcoxon sign-rank test, the p-value of the proposed model reached 0.98, indicating the effectiveness of the ARIMA-BPNN-SVR model.

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Section: Modern Photovoltaic Power Generation Forecasting Methodsmentioning

confidence: 99%

Section: Modern Photovoltaic Power Generation Forecasting Methodsmentioning

confidence: 99%

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Fan¹,

Hui-zhen²,

Chen³

et al. 2022

Glob. J. Energ. Technol. Res. Updat.

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“…Currently, researchers have conducted a range of studies on PV forecasts, and their adopted methods are typically divided into three categories, including physical approaches, statistical models, deep learning methods, and grey prediction models [9]. erefore, the appropriate and correct selection of a prediction technique is a necessary first step, and this action must be undertaken reasonably based on the rational analysis of the existing models and their forecasting performance.…”

Section: Previous Studies On Solar Photovoltaic Forecastsmentioning

confidence: 99%

“…Evidence shows that these hybrid models improved forecasting performance significantly compared to single-architecture models [26]. Ahmed et al [9] employed an ensemble-based LSTM algorithm constituted of ten component LSTM models. en, they used different data segmentations from three months to one data to compare the effects based on the various forecasting horizons from fourteen days to five minutes.…”

Section: Previous Studies On Solar Photovoltaic Forecastsmentioning

confidence: 99%

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A Novel Structure-Adaptive Fractional Bernoulli Grey Model for Solar Photovoltaic Forecasts

Huang

Ding

2022

Mathematical Problems in Engineering

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Since the limitation of carbon emissions, China’s photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department’s decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting.

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