Prediction of Photovoltaic Power Generation Based on General Regression and Back Propagation Neural Network

Zhong, Jiaqi; Liu, Luyao; Sun, Qiang; Wang, Xinyu

doi:10.1016/j.egypro.2018.09.173

Cited by 57 publications

(25 citation statements)

References 3 publications

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“…For the Ridge regression, SVR, AdaBoost, GBRT and BPNN, each input window explained in Part (6) of Section II, was transformed into a flattened format of L * (N −1), where L is equal to 24 lags and N − 1 is equal to 13 as the total number of variables (12 non-load variables selected through FS process and 1 load variable). As mentioned in Section II, Part (7), reducing one load variable from the input with the size of N indicates that the model that predicts future values of one target removes the values of other target from its input.…”

Section: In This Section We Describe the Experiments Conducted Inmentioning

confidence: 99%

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An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

et al. 2021

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This paper addresses the estimation of household communities' overall energy usage and solar energy production, considering different prediction horizons. Forecasting the electricity demand and energy generation of communities can help enrich the information available to energy grid operators to better plan their short-term supply. Moreover, households will increasingly need to know more about their usage and generation patterns to make wiser decisions on their appliance usage and energy-trading programs. The main issues to address here are the volatility of load consumption induced by the consumption behaviour and variability in solar output influenced by solar cells specifications, several meteorological variables, and contextual factors such as time and calendar information. To address these issues, we propose a predicting approach that first considers the highly influential factors and, second, benefits from an ensemble learning method where one Gradient Boosted Regression Tree algorithm is combined with several Sequenceto-Sequence LSTM networks. We conducted experiments on a public dataset provided by the Ausgrid Australian electricity distributor collected over three years. The proposed model's prediction performance was compared to those by contributing learners and by conventional ensembles. The obtained results have demonstrated the potential of the proposed predictor to improve short-term multi-step forecasting by providing more stable forecasts and more accurate estimations under different day types and meteorological conditions.

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Section: In This Section We Describe the Experiments Conducted Inmentioning

confidence: 99%

“…A recent correlation study reported in [6] shows that solar irradiance has the highest correlation with PV power output compared to other weather parameters. The result is validated for various weather conditions in [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

et al. 2021

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“…For this purpose, the Pearson correlation coefficient was used, which is widely used, especially in studies modeling atmospheric and/or meteorological variables. With the idea of estimating the result of a model in a simple and abbreviated way, this coefficient allowed us to know the parameters that most influence the output variable [29]. The coefficient values range from −1 to 1, with the correlation being positive when it is greater than zero and negative when it is less.…”

Section: Correlation Between the Variables And Soiling Lossesmentioning

confidence: 99%

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

2021

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Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE).

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“…The artificial neural network is an abstract mathematical algorithm model that utilizes a physical device to model the structure and function of a biological neural network; it consists of multiple input and single output neurons connected according to certain topological structures [34]. Currently, the BP neural network has been widely used in regression [35], recognition [36], and prediction analysis [37] due to its ability to address nonlinear and complex system problems; it has advantages for dealing with problems of fuzzy and inaccurate information while considering many factors. Theoretically, it has been proven that the three-layer BP neural network can realize arbitrary nonlinear mappings [34].…”

Section: Reputation Prediction Through the Bp Neural Networkmentioning

confidence: 99%

A Reputation Bootstrapping Model for E-Commerce Based on Fuzzy DEMATEL Method and Neural Network

2019

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Reputation evaluation plays an important role in determining the credibility of online entities. Especially in e-commerce systems, consumers usually give priority to this indicator when choosing vendors. Reputation bootstrapping, which can determine the default reputation value of new entities, is still a challenging problem. Most current bootstrapping methods fail to consider the complexity, the ambiguity of trust, or the reputation and internal correlation characteristics of influential factors in the prediction process. Therefore, in this paper, a novel reputation bootstrapping model that combines the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method with neural network prediction is established. First, we adopt a fuzzy multi-criteria decision-making model, named fuzzy DEMATEL method to discuss the intrinsic causal relationships between influential factors and identify the critical success factors (CSFs) for reputation estimation. Then, we adopt back propagation (BP) neural network to generalize the correlations between the CSFs and the initial reputation value. Finally, a case study is constructed to verify the proposed model. The experimental results indicate that the proposed model has better accuracy and efficiency compared with other reputation bootstrapping methods.

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Prediction of Photovoltaic Power Generation Based on General Regression and Back Propagation Neural Network

Cited by 57 publications

References 3 publications

An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

A Reputation Bootstrapping Model for E-Commerce Based on Fuzzy DEMATEL Method and Neural Network

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