Forecasting Hourly Solar Irradiance Using Hybrid Wavelet Transformation and Elman Model in Smart Grid

Huang, Xiaoqiao; Shi, Junsheng; Gao, Bixuan; Tai, Yonghang; Chen, Zaiqing; Zhang, Mengjie

doi:10.1109/access.2019.2943886

Cited by 54 publications

(23 citation statements)

References 45 publications

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“…The main difficulty in the PV system is the complexity, parasitic capacitance, harmonic distortion, and sophistication of the equation of current-voltage and power-voltage characteristics [10]. The relationship among PV current and voltage is both implicit and complex depending on certain variables, among them are the ambient temperature, solar irradiation, wind speed, and dust accumulation [11], [12]. On hot days, the cell module temperature can quickly be attained 70°C, where power energy output can drop significantly below nominal values [13].…”

Section: A Backgroundmentioning

confidence: 99%

“…This interval entails partitioning the dataset ( ) into = 2 ∆ℎ = 12 different datasets, and thus, = 12 ANNs will be built, optimized, and evaluated. Each dataset represents the timestamps, weather variables, and the corresponding power productions collected at each hour interval ℎ during the = 3.625 years, ℎ ∈ [1,12];  ∆ℎ = 3 hours. This interval entails partitioning the dataset ( ) into = 2 ∆ℎ = 8 different datasets, and thus, = 8 ANNs will be built, optimized, and evaluated.…”

Section: Influence Of Using Different Hour Intervals For Dataset Pmentioning

confidence: 99%

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A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

2020

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Power production prediction from Renewable Energy (RE) sources has been widely studied in the last decade. This is extremely important for utilities to counterpart electricity supply with consumer demands across centralized grid networks. In this context, we propose a local training strategy-based Artificial Neural Network (ANN) for predicting the power productions of solar Photovoltaic (PV) systems. Specifically, the timestamp, weather variables, and corresponding power productions collected locally at each hour interval h, h=[1,24] (i.e., an interval of ∆ℎ=1 hour), are exploited to build, optimize, and evaluate H=24 different ANNs for the 24 hourly solar PV production predictions. The proposed local training strategy-based ANN is expected to provide more accurate predictions with short computational times than those obtained by a single (i.e., H=1) ANN model (hereafter called benchmark) built, optimized, and evaluated globally on the entire available dataset. The proposed strategy is applied to a case study regarding a 264kWp solar PV system located in Amman, Jordan, and its effectiveness compared to the benchmark is verified by resorting to different performance metrics from the literature. Further, its effectiveness is verified and compared when Extreme Learning Machines (ELMs) are adopted instead of the ANNs, and when the Persistence model is used. The prediction performance of the two training strategies-based ANN is also investigated and compared in terms of i) different weather conditions (i.e., seasons) experienced by the solar PV system under study and ii) different hour intervals (i.e., ∆ℎ=2, 3, and 4 hours) used for partitioning the overall dataset and, thus, establishing the different ANNs (i.e., H =12, 8, and 6 models, respectively).

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Section: A Backgroundmentioning

confidence: 99%

Section: Influence Of Using Different Hour Intervals For Dataset Pmentioning

confidence: 99%

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

2020

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“…Besides, forecast skill (FS) is an indicator that compares a selected model with a reference model (usually with the persistence model), regardless of the prediction horizon and location [37,38], which is a fair-minded approach to evaluating performance in solar irradiance prediction, as described by the following equation [2]:…”

Section: Forecasting Accuracy Evaluationmentioning

confidence: 99%

A Comparison of Hour-Ahead Solar Irradiance Forecasting Models Based on LSTM Network

Huang

Zhang

et al. 2020

Mathematical Problems in Engineering

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The intermittence and fluctuation character of solar irradiance places severe limitations on most of its applications. The precise forecast of solar irradiance is the critical factor in predicting the output power of a photovoltaic power generation system. In the present study, Model I-A and Model II-B based on traditional long short-term memory (LSTM) are discussed, and the effects of different parameters are investigated; meanwhile, Model II-AC, Model II-AD, Model II-BC, and Model II-BD based on a novel LSTM-MLP structure with two-branch input are proposed for hour-ahead solar irradiance prediction. Different lagging time parameters and different main input and auxiliary input parameters have been discussed and analyzed. The proposed method is verified on real data over 5 years. The experimental results demonstrate that Model II-BD shows the best performance because it considers the weather information of the next moment, the root mean square error (RMSE) is 62.1618 W/m2, the normalized root mean square error (nRMSE) is 32.2702%, and the forecast skill (FS) is 0.4477. The proposed algorithm is 19.19% more accurate than the backpropagation neural network (BPNN) in terms of RMSE.

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“…Commonly used machine learning prediction methods include artificial neural networks (ANN), support vector machines (SVM), extreme learning machines (ELM), ensemble learning, and Gaussian process regression (GPR). ANNs [11] are comprised of flexible structures. If many neurons in the hidden layers are allowed, the model will have a strong nonlinear fitting capability.…”

Section: Introductionmentioning

confidence: 99%

Solar Power Forecasting Based on Domain Adaptive Learning

et al. 2020

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Solar power forecasting is critical to ensure the safety and stability of the power grid with high photovoltaic power penetration. Machine learning methods are compelling in solar forecasting. These methods can capture the complex coupling relationship between different meteorological factors without physical modeling. Most of the existing machine learning based forecasts follow the batch learning manner. Once the training is completed, the structure and parameters of the model are usually no longer adjusted. However, the climate is complex and dynamic. It is difficult for a fixed model to adapt to the climate characteristics of different regions or periods. Therefore, an online domain adaptive learning approach is proposed in this paper. Knowledge can be selectively accumulated or forgotten in its iterative process. As weather changes, the model can dynamically adjust its structure to adapt to the latest weather conditions. Unlike existing adaptive iterative methods, the proposed adaptive learning approach does not rely on the labels of the test data in the updating process. Experiments show that this method can effectively track changes in data distribution and obtain reliable prediction results. INDEX TERMS Solar power forecasting, adaptive learning, neural networks, ensemble learning.

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Forecasting Hourly Solar Irradiance Using Hybrid Wavelet Transformation and Elman Model in Smart Grid

Cited by 54 publications

References 45 publications

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

A Comparison of Hour-Ahead Solar Irradiance Forecasting Models Based on LSTM Network

Solar Power Forecasting Based on Domain Adaptive Learning

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