Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Ma, Wanxing; Chen, Zhimin; Zhu, Qing

doi:10.3390/electronics9101717

Cited by 12 publications

(9 citation statements)

References 37 publications

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“…Based on these results, MG data from high correlation periods were utilised to build and train a machine-learning-based prediction model. A simple linear regression (LR) model was employed as in [18] with a set of the top 5 correlating weather parameters chosen as inputs and historical microgrid data as target outputs [19] [20]. Fig.…”

Section: ) Data For Advanced Researchmentioning

confidence: 99%

Demonstrating the Value of Generating and Sharing Data on Off-Grid Energy Systems: A Case Study from Malawi

Frame,

Mafuta,

Galloway

et al. 2023

2023 IEEE Global Humanitarian Technology Conference (GHTC)

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To achieve electricity access in sub-Saharan Africa, off-grid Distributed Energy Resource Systems, such as microgrids, are required. Sustainability of these systems requires improved business models and efficient maintenance and operations frameworks. However, a lack of technical and economic data from the existing installation base hampers the necessary learning and innovation. This paper describes a case study deployment of DER systems in Malawi, demonstrating the application and benefits of high levels of instrumentation and monitoring. A proposed classification of minimum, preferred and desirable levels of data gathering and sharing is offered as a key recommendation for future DER system deployments in Malawi.

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Section: ) Data For Advanced Researchmentioning

confidence: 99%

Demonstrating the Value of Generating and Sharing Data on Off-Grid Energy Systems: A Case Study from Malawi

Frame,

Mafuta,

Galloway

et al. 2023

2023 IEEE Global Humanitarian Technology Conference (GHTC)

View full text Add to dashboard Cite

show abstract

“…Because of these characteristics, the proportion of solar PV generation and use is expected to continue to increase in the future [7,8]. Predicting solar PV power generation is very important for the operation of a sustainable energy system, including maintaining a balance between energy production and consumption and optimal utilization of energy resources [9]. However, compared to traditional energy sources, the dependence of solar PV power generation on environmental factors (e.g., sensitivity to changes in weather conditions) introduces significant variability.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting

Oh,

So,

et al. 2024

Electronics

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Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability to effectively learn unstable environmental variables and their complex interactions. However, NNs are limited in their practical industrial application in the energy sector because the optimization of the model structure or hyperparameters is a complex and time-consuming task. This paper proposes a two-stage NN optimization method for robust solar PV power forecasting. First, the solar PV power dataset is divided into training and test sets. In the training set, several NN models with different numbers of hidden layers are constructed, and Optuna is applied to select the optimal hyperparameter values for each model. Next, the optimized NN models for each layer are used to generate estimation and prediction values with fivefold cross-validation on the training and test sets, respectively. Finally, a random forest is used to learn the estimation values, and the prediction values from the test set are used as input to predict the final solar PV power. As a result of experiments in the Incheon area, the proposed method is not only easy to model but also outperforms several forecasting models. As a case in point, with the New-Incheon Sonae dataset—one of three from various Incheon locations—the proposed method achieved an average mean absolute error (MAE) of 149.53 kW and root mean squared error (RMSE) of 202.00 kW. These figures significantly outperform the benchmarks of attention mechanism-based deep learning models, with average scores of 169.87 kW for MAE and 232.55 kW for RMSE, signaling an advance that is expected to make a significant contribution to South Korea's energy industry.

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“…Traditional machine-learning techniques, such as principal-component analysis (PCA) [11,12], variable modulus decomposition (VMD) [13], and random forest (RF) [14], are utilized for data-feature extraction and prediction. Additionally, support-vector machine (SVM) [15], artificial neural network (ANN) [16], Elman neural network [17], radial basis function neural network (RBF) [18], Bayesian methods [19], among others, are employed for power-generation prediction. For instance, in references [20,21], SVM was utilized to enhance the accuracy of PV power-generation prediction.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model

Wang,

Jia,

et al. 2024

Sustainability

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Precise prediction of the power generation of photovoltaic (PV) stations on the island contributes to efficiently utilizing and developing abundant solar energy resources along the coast. In this work, a hybrid short-term prediction model (ICMIC-POA-CNN-BIGRU) was proposed to study the output of a fishing–solar complementary PV station with high humidity on the island. ICMIC chaotic mapping was used to optimize the initial position of the pelican optimization algorithm (POA) population, enhancing the global search ability. Then, ICMIC-POA performed hyperparameter debugging and L2-regularization coefficient optimization on CNN-BIGRU (convolutional neural network and bidirectional gated recurrent unit). The L2-regularization technique optimized the loss curve and over-fitting problem in the CNN-BIGRU training process. To compare the prediction effect with the other five models, three typical days (sunny, cloudy, and rainy) were selected to establish the model, and six evaluation indexes were used to evaluate the prediction performance. The results show that the model proposed in this work shows stronger robustness and generalization ability. K-fold cross-validation verified the prediction effects of three models established by different datasets for three consecutive days and five consecutive days. Compared with the CNN-BIGRU model, the RMSE values of the newly proposed model were reduced by 64.08%, 46.14%, 57.59%, 60.61%, and 34.04%, respectively, in sunny, cloudy, rainy, continuous prediction 3 days, and 5 days. The average value of the determination coefficient R2 of the 20 experiments was 0.98372 on sunny days, 0.97589 on cloudy days, and 0.98735 on rainy days.

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Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Cited by 12 publications

References 37 publications

Demonstrating the Value of Generating and Sharing Data on Off-Grid Energy Systems: A Case Study from Malawi

Demonstrating the Value of Generating and Sharing Data on Off-Grid Energy Systems: A Case Study from Malawi

Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting

Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model

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