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1
Fan, Yuanliang, Han Wu, Jianli Lin, Zewen Li, Lingfei Li, Xinghua Huang, Weiming Chen, and Beibei Chen. "A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing." Journal of Physics: Conference Series 2876, no. 1 (November 1, 2024): 012050. http://dx.doi.org/10.1088/1742-6596/2876/1/012050.
Повний текст джерелаАнотація:
Abstract In recent years, an immense amount of distributed photovoltaics are integrated into low-voltage distribution network, producing a substantial volume of operational data. The centralized cloud data center cannot process massive amounts of data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low-voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enables local data processing to improve the forecasting service’s real-time reliability. In this regard, this paper proposes a distributed photovoltaic short-term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which removes redundant parts of the model, resulting in a compact and efficient forecasting model. By validating real-world datasets, the results demonstrate that the model presented in this article has a smaller size and higher forecasting accuracy than other state-of-the-art forecasting models.
2
Yang, Shu-Xia, Yang Zhang, and Xiao-Yu Cheng. "Economic modeling of distributed photovoltaic penetration considering subsidies and countywide promotion policy: An empirical study in Beijing." Journal of Renewable and Sustainable Energy 14, no. 5 (September 2022): 055301. http://dx.doi.org/10.1063/5.0102574.
Повний текст джерелаАнотація:
Distributed photovoltaic power generation will not only help to achieve the strategic targets of peaking carbon emissions and carbon neutrality but also cause a series of impacts on the power grid at the same time. Forecasting the long-term development of regionally distributed photovoltaics can provide a reference for power grid planning and stable operation. In this paper, considering the effect of factors such as subsidies and countywide promotion policy of photovoltaics, a forecasting model for the development tendency of regionally distributed photovoltaics based on system dynamics is established. Then, taking Beijing as an example, an empirical analysis is carried out, and the effect of the proportion of self-consumption and the time when the subsidy is adjusted on distributed photovoltaic penetration is explored through sensitivity analysis. The simulation results show that the installed capacity achieved by the countywide promotion policy will become the main source of the installed capacity growth of distributed photovoltaics in Beijing after 2024. To continuously boost distributed photovoltaic penetration, relevant policymakers should consider the appropriate time when the subsidy is adjusted according to the installation cost of photovoltaic systems.
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Matushkin, Dmytro. "PHOTOVOLTAIC GENERATION FORECASTING MODELS: CONCEPTUAL ENSEMBLE ARCHITECTURES." System Research in Energy 2024, no. 4 (November 29, 2024): 56–64. https://doi.org/10.15407/srenergy2024.04.056.
Повний текст джерелаАнотація:
The decisions regarding power regulation, energy resource planning, and integrating “green” energy into the electrical grid hinge on precise probabilistic forecasts. One of the potential strategies to enhance forecast accuracy is the utilization of ensemble forecasting methods. They represent an approach where multiple models collaborate to achieve superior results compared to what a single model could produce independently. These methods can be categorized into two main categories: competitive and collaborative ensembles. Competitive ensembles harness the diversity of parameters and data to create a rich pool of base models. This approach may encompass statistical analysis, noise filtering, and anomaly elimination. On the other hand, collaborative ensembles rely on the interaction among models to achieve better outcomes. These methods encompass strategies such as weighted predictions, voting, aggregation, and a combination of model results. The research of ensemble forecasting methods in the context of photovoltaic generation is highly relevant, as solar energy represents a crucial source of renewable energy. Accurate predictions of solar energy production address the challenges related to the efficient utilization of photovoltaic panels and their integration into the overall energy system. This paper investigates conceptual ensemble architectures for photovoltaic energy forecasting. These architectures encompass various methods of aggregating base models within an ensemble, allowing for the consideration of different aspects and peculiarities of solar data, such as solar irradiation intensity, meteorological conditions, geographic factors, and more. These conceptual models are developed based on well-established statistical, machine learning, and artificial intelligence methods. Therefore, this paper provides an overview of ensemble forecasting methods for renewable energy, covering competitive and collaborative ensembles, as well as developing conceptual models for solar energy forecasting. This work aims to elevate the accuracy and efficiency of forecasts in the realm of renewable energy, representing a significant step in the advancement of sustainable and environmentally friendly energy production. Keywords: probabilistic solar forecasting, ensemble model, forecast combination, competitive ensembles, collaborative ensembles, conceptual models.
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El hendouzi, Abdelhakim, and Abdennaser Bourouhou. "Solar Photovoltaic Power Forecasting." Journal of Electrical and Computer Engineering 2020 (December 31, 2020): 1–21. http://dx.doi.org/10.1155/2020/8819925.
Повний текст джерелаАнотація:
The management of clean energy is usually the key for environmental, economic, and sustainable developments. In the meantime, the energy management system (EMS) ensures the clean energy which includes many sources grouped in a small power plant such as microgrid (MG). In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy. The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator. At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons. Furthermore, this current review paper can support the tenders in the PV power forecasting.
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Chin, Kho Lee. "A Case Study of Using Long Short-Term Memory (LSTM) Algorithm in Solar Photovoltaic Power Forecasting." ASM Science Journal 18 (December 26, 2023): 1–8. http://dx.doi.org/10.32802/asmscj.2023.1162.
Повний текст джерелаАнотація:
Solar photovoltaic power plays an important role in distributed energy resources. The number of solar-powered electricity generation has increased steadily in recent years all over the world. This happens because it produces clean energy, and solar photovoltaic technology is continuously developing. One of the challenges in solar photovoltaic is that power generation is highly dependent on the dynamic changes of environmental parameters and asset operating conditions. Solar power forecasting can be a possible solution to maximise the electricity generation capability of the solar photovoltaic system. This study implements the deep learning method, long short-term memory (LSTM) models for time series forecasting in solar photovoltaic power generation forecasting. The data set collected by The Ravina Project from 2010 to 2014 is used as the training data in the simulations. The root mean square value is used in this study to measure the forecasting error. The results show that the deep learning algorithm provides reliable forecasting results.
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Antonanzas, J., N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres. "Review of photovoltaic power forecasting." Solar Energy 136 (October 2016): 78–111. http://dx.doi.org/10.1016/j.solener.2016.06.069.
Повний текст джерела
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Poti, Keaobaka D., Raj M. Naidoo, Nsilulu T. Mbungu, and Ramesh C. Bansal. "Intelligent solar photovoltaic power forecasting." Energy Reports 9 (October 2023): 343–52. http://dx.doi.org/10.1016/j.egyr.2023.09.004.
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Okhorzina, Alena, Alexey Yurchenko, and Artem Kozloff. "Autonomous Solar-Wind Power Forecasting Systems." Advanced Materials Research 1097 (April 2015): 59–62. http://dx.doi.org/10.4028/www.scientific.net/amr.1097.59.
Повний текст джерелаАнотація:
The paper reports on the results of climatic testing of silicon photovoltaic modules and photovoltaic power systems conducted in Russia (Siberia and the Far East). The monitoring system to control the power system work was developed. Testing over 17 years and a large amount of experimental studies enabled us to develop a precise mathematical model of the photovoltaic module in natural environment taking into account climatic and hardware factors.
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Xinhui, Du, Wang Shuai, and Zhang Juan. "Research on Marine Photovoltaic Power Forecasting Based on Wavelet Transform and Echo State Network." Polish Maritime Research 24, s2 (August 28, 2017): 53–59. http://dx.doi.org/10.1515/pomr-2017-0064.
Повний текст джерелаАнотація:
Abstract With the rapid development of photovoltaic power generation technology, photovoltaic power generation system has gradually become an important component of the integrated energy system of marine. High precision short-term photovoltaic power generation forecasting is becoming one of the key technologies in ship energy saving and ship energy efficiency improving. Aiming at the characteristics of marine photovoltaic power generation system, we designed a highprecision power forecasting model (WT+ESN) for marine photovoltaic power generation system with anti-marine environmental interference. In this model, the information mining of the photovoltaic system in marine environment is carried out based on wavelet theory, then the forecasting model basing on echo state network is construct ed. Lastly, three kinds of error metrics are compared with the three traditional models by Matlab, the result shows that the model has high forecasting accuracy and strong robustness to marine environmental factors, which is of great significance to save fuel for ships, improve the energy utilization rate and assist the power dispatching and fuel dispatching of the marine power generation system.
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Wang, Yusen, Wenlong Liao, and Yuqing Chang. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting." Energies 11, no. 8 (August 18, 2018): 2163. http://dx.doi.org/10.3390/en11082163.
Повний текст джерелаАнотація:
Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods.
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Lu, Zhiying, Wenpeng Chen, Qin Yan, Xin Li, and Bing Nie. "Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather." Sustainability 15, no. 23 (November 23, 2023): 16233. http://dx.doi.org/10.3390/su152316233.
Повний текст джерелаАнотація:
Haze constitutes a pivotal meteorological variable with notable implications for photovoltaic power forecasting. The presence of haze is anticipated to lead to a reduction in the output power of photovoltaic plants. Therefore, achieving precise forecasts of photovoltaic power in hazy conditions holds paramount significance. This study introduces a novel approach to forecasting photovoltaic power under haze conditions, leveraging ground-based cloud images. Firstly, the aerosol scattering coefficient is introduced as a pivotal parameter for characterizing photovoltaic power fluctuations influenced by haze. Additionally, other features, such as sky cloud cover, color attributes, light intensity, and texture characteristics, are considered. Subsequently, the Spearman correlation coefficient is applied to calculate the correlation between feature sequences and photovoltaic power. Effective features are then selected as inputs and three models—LSTM, SVM, and XGBoost—are employed for training and performance analysis. After comparing with existing technologies, the predicted results have achieved the best performance. Finally, using actual data, the effectiveness of the aerosol scattering coefficient is confirmed, by exhibiting the highest correlation index, as a pivotal parameter for forecasting photovoltaic output under the influence of haze. The results demonstrate that the aerosol scattering coefficient enhances the forecast accuracy of photovoltaic power in both heavy and light haze conditions by 1.083% and 0.599%, respectively, while exerting minimal influence on clear days. Upon comprehensive evaluation, it is evident that the proposed forecasting method in this study offers substantial advantages for accurately predicting photovoltaic power output in hazy weather scenarios.
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Feng, Dongyang, Hanjin Zhang, and Zhijin Wang. "Hourly photovoltaic power prediction based on signal decomposition and deep learning." Journal of Physics: Conference Series 2728, no. 1 (March 1, 2024): 012011. http://dx.doi.org/10.1088/1742-6596/2728/1/012011.
Повний текст джерелаАнотація:
Abstract Accurate photovoltaic (PV) power prediction is important for the utilization of solar energy resources. However, PV power is non-stationary due to the variable influence of meteorological factors, which poses a challenge for accurate forecasting. In this paper, a hybrid method based on signal decomposition and a deep learning model is proposed. The hybrid model integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the Informer model. The CEEMDAN algorithm is used to separate different modes from the photovoltaic power sequence, enhancing its predictability. The deep learning model, the Informer, is employed to capture the complex relationship between photovoltaic power data and its historical data as well as external meteorological factors, ultimately enabling multi-step forecasting of photovoltaic power data. In hourly PV power forecasting experiments using a public dataset, the model exhibits significant performance improvements when compared to benchmark models such as LSTM, GRU, and Transformer. Specifically, the RMSE reduces by 6.07%-34.74% and the MAE reduces by 7.07%-37.5%. The results demonstrate that the hybrid model exhibits accurate predictive performance in the task of hourly photovoltaic power forecasting.
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El hendouzi, Abdelhakim, Abdennaser Bourouhou, and Omar Ansari. "The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting." Journal of Electrical and Computer Engineering 2020 (April 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/9586707.
Повний текст джерелаАнотація:
The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.
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Zhang, Xiao, Runjie Shen, and Yiying Wang. "A Combined Method of Two-model based on Forecasting Meteorological Data for Photovoltaic Power Generation Forecasting." E3S Web of Conferences 185 (2020): 01053. http://dx.doi.org/10.1051/e3sconf/202018501053.
Повний текст джерелаАнотація:
Under the background of the continuous development of photovoltaic power generation technology, accurate prediction of photovoltaic output power has become an important subject. In this paper, a combined method of two-model based on forecasting meteorological data for photovoltaic power generation forecasting is proposed. To solve the problem of the adaptability of a single model, two different models are used according to the different types of output power characteristics. The K-means clustering algorithm is used to classify different weather types according to the historical meteorological data. After predicting the irradiance and temperature of the period to be predicted and classifying the period into different types, the photovoltaic output power is predicted by a suitable model. The two prediction models are the Wavelet- Decomposition-ARIMA model and EDM-SA-DBN model, which are suitable for periods with larger and smaller fluctuation amplitude of photovoltaic output, respectively. Wavelet decomposition can refine the data with large fluctuations on multiple scales, make the data smooth, and improve the prediction accuracy of the Autoregressive Integrated Moving Average model (ARIMA). The Deep Belief Network (DBN) can effectively process a large number of complex data and deep mining the data features. While the empirical mode decomposition (EMD) can decompose the more stable data and amplify the details in the signal as much as possible. Meanwhile, the simulated annealing algorithm (SA) can avoid the network falling into a local optimal solution and improve the prediction accuracy. This paper uses a large number of photovoltaic power station data for experimental verification. The results show that this combined model has high accuracy and generalization ability.
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Gu, Bo, Xi Li, Fengliang Xu, Xiaopeng Yang, Fayi Wang, and Pengzhan Wang. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM." Sustainability 15, no. 8 (April 12, 2023): 6538. http://dx.doi.org/10.3390/su15086538.
Повний текст джерелаАнотація:
Accurate forecasting of photovoltaic (PV) power is of great significance for the safe, stable, and economical operation of power grids. Therefore, a day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis method based on WT-CNN-BiLSTM-AM-GMM is proposed in this paper. Wavelet transform (WT) is used to decompose numerical weather prediction (NWP) data and photovoltaic power data into frequency data with time information, which eliminates the influence of randomness and volatility in the data information on the forecasting accuracy. A convolutional neural network (CNN) is used to deeply mine the seasonal characteristics of the input data and the correlation characteristics between the input data. The bidirectional long short-term memory network (BiLSTM) is used to deeply explore the temporal correlation of the input data series. To reflect the different influences of the input data sequence on the model forecasting accuracy, the weight of the calculated value of the BiLSTM model for each input data is adaptively adjusted using the attention mechanism (AM) algorithm according to the data sequence, which further improves the model forecasting accuracy. To accurately calculate the probability density distribution characteristics of photovoltaic forecasting errors, the Gaussian mixture model (GMM) method was used to calculate the probability density distribution of forecasting errors, and the confidence interval of the day-ahead PPF was calculated. Using a photovoltaic power station as the calculation object, the forecasting results of the WT-CNN-BiLSTM-AM, CNN-BiLSTM, WT-CNN-BiLSTM, long short-term memory network (LSTM), gate recurrent unit (GRU), and PSO-BP models were compared and analyzed. The calculation results show that the forecasting accuracy of the WT-CNN-BiLSTM-AM model is higher than that of the other models. The confidence interval coverage calculated from the GMM is greater than the given confidence level.
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Li, Dengxuan, Wenwen Ma, Siyu Hu, Fang Qin, Weidong Chen, Huang Ding, and Yutong Han. "The Method of Photovoltaic Power Forecast based on Seasonal Classification and Limit Learning Machine." Journal of Physics: Conference Series 2474, no. 1 (April 1, 2023): 012065. http://dx.doi.org/10.1088/1742-6596/2474/1/012065.
Повний текст джерелаАнотація:
Abstract The seasonal distribution characteristics of photovoltaic power plant output fluctuation are analyzed, and a short-term power forecasting method based on seasonal classification is proposed. Firstly, the seasonal distribution characteristics of photovoltaic output and its fluctuation are analyzed. Secondly, the forecasting model of photovoltaic output in different seasons is established by the Limit Learning Machine neural network. Finally, an empirical analysis is carried out by using photovoltaic output data. The results show that the seasonal classification method of short-term PV power forecast is better than the unclassified model.
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Iheanetu, Kelachukwu J. "Solar Photovoltaic Power Forecasting: A Review." Sustainability 14, no. 24 (December 19, 2022): 17005. http://dx.doi.org/10.3390/su142417005.
Повний текст джерелаАнотація:
The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.
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Eroshenko, Stanislav A., Alexandra I. Khalyasmaa, Denis A. Snegirev, Valeria V. Dubailova, Alexey M. Romanov, and Denis N. Butusov. "The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation." Applied Sciences 10, no. 22 (November 21, 2020): 8265. http://dx.doi.org/10.3390/app10228265.
Повний текст джерелаАнотація:
The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.
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Kodaira, Daisuke, Kazuki Tsukazaki, Taiki Kure, and Junji Kondoh. "Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations." Energies 14, no. 21 (November 4, 2021): 7340. http://dx.doi.org/10.3390/en14217340.
Повний текст джерелаАнотація:
Photovoltaic (PV) generation is potentially uncertain. Probabilistic PV generation forecasting methods have been proposed with prediction intervals (PIs) to evaluate the uncertainty quantitively. However, few studies have applied PIs to geographically distributed PVs in a specific area. In this study, a two-step probabilistic forecast scheme is proposed for geographically distributed PV generation forecasting. Each step of the proposed scheme adopts ensemble forecasting based on three different machine-learning methods. When individual PV generation is forecasted, the proposed scheme utilizes surrounding PVs’ past data to train the ensemble forecasting model. In this case study, the proposed scheme was compared with conventional non-multistep forecasting. The proposed scheme improved the reliability of the PIs and deterministic PV forecasting results through 30 days of continuous operation with real data in Japan.
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Kim, Taeyoung, and Jinho Kim. "A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation." Energies 14, no. 14 (July 14, 2021): 4256. http://dx.doi.org/10.3390/en14144256.
Повний текст джерелаАнотація:
Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation but also PV capacity information is invisible due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved.
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Li, Wang, Zhang, Xin, and Liu. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach." Energies 12, no. 13 (July 1, 2019): 2538. http://dx.doi.org/10.3390/en12132538.
Повний текст джерелаАнотація:
The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.
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Qin, Weiming, Wenjing Guo, Wenjing Li, Yumin Liu, Liyuan Gao, Wei Zhang, and Jingwen Lin. "Photovoltaic power prediction based on multi-layer fusion model." Journal of Physics: Conference Series 2355, no. 1 (October 1, 2022): 012046. http://dx.doi.org/10.1088/1742-6596/2355/1/012046.
Повний текст джерелаАнотація:
Abstract Accurate forecasting of photovoltaic power output can provide technical guidance for smooth grid connection of photovoltaic power plants and avoid grid energy quality problems caused by large fluctuations in photovoltaic power. Therefore, it is very important to obtain more accurate photovoltaic energy forecast data. The traditional photovoltaic power forecasting model has the problems of low forecast accuracy and hysteresis.to further improve the prediction accuracy of pv power, this paper proposes a multi-layer fusion PV power prediction model based on dynamic modification of the weight parameters of each model. The model's benchmark algorithms include XGBoost, LigthGBM and ConvLSTM. In the case study, real photovoltaic station data were used to verify the simulation,The forecast results show that the model proposed in this paper has a better effect than any single model and has strong practicability.
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Guo, Hua Ping, Shuang Hui Wu, Zhao Qing Wang, and Chang An Wu. "Linear Regression for Forecasting Photovoltaic Power Generation." Applied Mechanics and Materials 494-495 (February 2014): 1771–74. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1771.
Повний текст джерелаАнотація:
One key issue for knowledge discovery is to build a model with simple structure, high performance and interpretability. Linear regression is simple and interpretable model comparing to other models such as neural network. This paper introduces linear regression into photovoltaic power forecasting. Experimental results on the data set collected by Zhongwei third photovoltaic power station of Ningxia Jinyang new energy Co., Ltd. show that, compared with neural network, linear regression performs better generated power forecasting.
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Wei, H., X. Chen, and W. Mi. "An Attention-Based Photovoltaic Forecasting Scheme Combined with LSTM Model." Journal of Physics: Conference Series 2141, no. 1 (December 1, 2021): 012017. http://dx.doi.org/10.1088/1742-6596/2141/1/012017.
Повний текст джерелаАнотація:
Abstract With the development of new energy around the world, the proportion of photovoltaic energy used as a clean energy in the distribution network is gradually rising. The forecast of photovoltaic power plants is vital to many energy providers for their marketing analysis. Thus, photovoltaic forecasting has become an important research direction at present. However, owing to the high volatility and intermittent characteristics of photovoltaic power generation, it is still a challenge to predict photovoltaic power accurately. As far as traditional photovoltaic forecasting methods are concerned, SVM and ARIMA, as machine learning methods can solve the timing prediction problem of certain scenarios, but they are often not appropriate for some time series closely related to features. To address this problem, this paper proposes a short-term photovoltaic load forecasting model based on the Attention mechanism and LSTM model. Firstly, the correlation coefficient and LASSO regression are used for feature selection to filter out redundant features. Secondly, a long short-term memory network (LSTM) is used to make predictions to solve the problem of gradient disappearance during model training. Finally, the Attention mechanism is added to better capture feature weights and further improve the prediction accuracy of the training model. The proposed method can predict the change trend well. Comparative results confirm that the proposed method with feature selection can has better effect than ARIMA, SVM, and ELM.
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Feng, Siling, Ruitao Chen, Mengxing Huang, Yuanyuan Wu, and Huizhou Liu. "Multisite Long-Term Photovoltaic Forecasting Model Based on VACI." Electronics 13, no. 14 (July 17, 2024): 2806. http://dx.doi.org/10.3390/electronics13142806.
Повний текст джерелаАнотація:
In the field of photovoltaic (PV) power prediction, long-term forecasting, which is more challenging than short-term forecasting, can provide more comprehensive and forward-looking guidance. Currently, significant achievements have been made in the field of short-term forecasting for PV power, but inadequate attention has been paid to long-term forecasting. Additionally, multivariate global forecasting across multiple sites and the limited historical time series data available further increase the difficulty of prediction. To address these challenges, we propose a variable–adaptive channel-independent architecture (VACI) and design a deep tree-structured multi-scale gated component named DTM block for this architecture. Subsequently, we construct a specific forecasting model called DTMGNet. Unlike channel-independent modeling and channel-dependent modeling, the VACI integrates the advantages of both and emphasizes the diversity of training data and the model’s adaptability to different variables across channels. Finally, the effectiveness of the DTM block is empirically validated using the real-world solar energy benchmark dataset. And on this dataset, the multivariate long-term forecasting performance of DTMGNet achieved state-of-the-art (SOTA) levels, particularly making significant breakthroughs in the 720-step ultra-long forecasting window, where it reduced the MSE metric below 0.2 for the first time (from 0.215 to 0.199), representing a reduction of 7.44%.
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Khumma, Kriangkamon, and Kreangsak Tamee. "Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 2 (March 14, 2020): 188–95. http://dx.doi.org/10.37936/ecti-cit.2019132.198498.
Повний текст джерелаАнотація:
This paper proposes a photovoltaic (PV) power forecasting model, using the application of a Gaussian blur algorithm filtering technique to estimate power output and the creation of a stochastic forecasting model. As a result, affected power can be forecasted from stochastic factors with machine learning and an artificial neural network. This model focuses on very short-term forecasting over a five minute period. As it uses only endogenous data, no exogenous data is needed.
To evaluate the model, results were compared to the persistence model, which has good short-term forecasting accuracy. This proposed PV forecasting model gained higher accuracy than the persistence model using stochastic factors.
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Yu, Dukhwan, Seowoo Lee, Sangwon Lee, Wonik Choi, and Ling Liu. "Forecasting Photovoltaic Power Generation Using Satellite Images." Energies 13, no. 24 (December 14, 2020): 6603. http://dx.doi.org/10.3390/en13246603.
Повний текст джерелаАнотація:
As the relative importance of renewable energy in electric power systems increases, the prediction of photovoltaic (PV) power generation has become a crucial technology, for improving stability in the operation of next-generation power systems, such as microgrid and virtual power plants (VPP). In order to improve the accuracy of PV power generation forecasting, a fair amount of research has been applied to weather forecast data (to a learning process). Despite these efforts, the problems of forecasting PV power generation remains challenging since existing methods show limited accuracy due to inappropriate cloud amount forecast data, which are strongly correlated with PV power generation. To address this problem, we propose a PV power forecasting model, including a cloud amount forecasting network trained with satellite images. In addition, our proposed model adopts convolutional self-attention to effectively capture historical features, and thus acquire helpful information from weather forecasts. To show the efficacy of the proposed cloud amount forecast network, we conduct extensive experiments on PV power generation forecasting with and without the cloud amount forecast network. The experimental results show that the Mean Absolute Percentage Error (MAPE) of our proposed prediction model, combined with the cloud amount forecast network, are reduced by 22.5% compared to the model without the cloud amount forecast network.
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Zhang, Tao, Ligang Yang, Ruijin Zhu, and Chao Yuan. "The application and optimization of scene reduction algorithm in integrated prediction of wind and photovoltaic energy." Journal of Physics: Conference Series 2903, no. 1 (November 1, 2024): 012042. https://doi.org/10.1088/1742-6596/2903/1/012042.
Повний текст джерелаАнотація:
Abstract Wind power and photovoltaic energy, as the most representative clean energy sources, have experienced rapid development. However, they are highly dependent on meteorological conditions and have poor predictability, posing a significant challenge for the integrated forecasting of wind power and photovoltaic energy. This paper investigates a scenario reduction algorithm based on Latin hypercube sampling and probability distribution to optimize the integrated forecasting of wind power and photovoltaic energy. By generating a large number of wind power and photovoltaic scenarios and utilizing Euclidean distance and probability distribution calculations, the scenarios are streamlined. The algorithm is implemented in MATLAB, and the effectiveness and superiority of the algorithm are demonstrated through detailed experiments and plots.
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Batsala, Ya V., I. V. Hlad, I. I. Yaremak, and O. I. Kiianiuk. "Mathematical model for forecasting the process of electric power generation by photoelectric stations." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 1 (2021): 111–16. http://dx.doi.org/10.33271/nvngu/2021-1/111.
Повний текст джерелаАнотація:
Purpose. Improving the efficiency of photovoltaic power plants in power systems by creating a model for forecasting the amount of electricity produced in the form of a harmonic function and determining the prospects for using the selected mathematical software to develop software applications. Methodology. To determine the amount of electricity generated by photovoltaic plants per day and year, statistical methods are applied using the harmonic function which allows taking into account the main meteorological factors of power change of photomodules. A technique is proposed for taking into account the level of generation by photovoltaic stations to track changes in voltage levels in the connection nodes. Findings. Mathematical models for forecasting the electricity generation of photovoltaic stations for different time ranges are built. The influence of weather factors, the length of daylight and the structure of the local generation system on the level of electricity generated by photovoltaic plants is investigated. Necessity is conditioned to use a harmonic function for forecasting the amount of electricity produced, which improves the efficiency of calculations for new and existing power plants. Originality. The factors of the influence of daylight hours and cloudiness on the level of electricity generation by photovoltaic stations are taken into account, as well as meteorological data that make it possible to predict the value of the amount of electricity generated for a certain period of time. The dependences of the amount of generated electricity by photovoltaic stations are obtained in the form of a harmonious function with reference to a coefficient that takes into account the cloud level for predicting generation volumes. Practical value. Created mathematical models of forecasting by means of harmonic function and analysis of voltage change in nodes of local networks allow increasing the efficiency of photovoltaic stations, simplify calculation of change of levels of voltages in a electric network, the forecasted values of the generated electric power on the day ahead system on the basis of duration of the light day, meteorological data and other external factors at commissioning of photovoltaic stations.
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Pattanaik, Debasish, Sanhita Mishra, Ganesh Prasad Khuntia, Ritesh Dash, and Sarat Chandra Swain. "An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network." Open Engineering 10, no. 1 (July 7, 2020): 630–41. http://dx.doi.org/10.1515/eng-2020-0073.
Повний текст джерелаАнотація:
AbstractAnalysing the Output Power of a Solar Photo-voltaic System at the design stage and at the same time predicting the performance of solar PV System under different weather condition is a primary work i.e. to be carried out before any installation. Due to large penetration of solar Photovoltaic system into the traditional grid and increase in the construction of smart grid, now it is required to inject a very clean and economic power into the grid so that grid disturbance can be avoided. The level of solar Power that can be generated by a solar photovoltaic system depends upon the environment in which it is operated and two other important factor like the amount of solar insolation and temperature. As these two factors are intermittent in nature hence forecasting the output of solar photovoltaic system is the most difficult work. In this paper a comparative analysis of different solar photovoltaic forecasting method were presented. A MATLAB Simulink model based on Real time data which were collected from Odisha (20.9517∘N, 85.0985∘E), India. were used in the model for forecasting performance of solar photovoltaic system.
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Song, Weiye, Meining Jiao, Shuang Han, Jie Yan, Han Wang, and Yongqian Liu. "Multi-Task neural network model considering low power output risk for short-term photovoltaic forecasting." Journal of Physics: Conference Series 2771, no. 1 (May 1, 2024): 012023. http://dx.doi.org/10.1088/1742-6596/2771/1/012023.
Повний текст джерелаАнотація:
Abstract As the proportion of installed photovoltaic power generation continues to increase, low output under the influence of large-scale weather systems has an increasingly significant influence on the power grid. There is an urgent need to improve photovoltaic power generation forecasting accuracy and reduce the risk of insufficient output in forecast results. To this end, a multi-task neural network model considering low power output risk (LPOMTN) for short-term photovoltaic forecasting is proposed. First, a numerical weather forecast encoder based on multi-scale CNN-Attention is established, which can extract multi-time scale photovoltaic output characteristics. Then a low-output day prediction module based on parallel CNN decoding and a photovoltaic low-output process prediction module based on the clear-sky model and LSTM were established. The latter uses the prediction results of the former as input, and the two modules perform training modeling in a multi-task learning manner to strengthen the model’s sensitivity to low-output states and improve the accuracy of low-output process predictions. The calculation example results show that LPOMTN has higher average forecasting accuracy for power output processes compared to methods such as XGBoost.
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Fan, Guo-Feng, Hui-Zhen Wei, Meng-Yao Chen, and Wei-Chiang Hong. "Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model." Global Journal of Energy Technology Research Updates 9 (August 5, 2022): 18–38. http://dx.doi.org/10.15377/2409-5818.2022.09.2.
Повний текст джерелаАнотація:
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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Khalyasmaa, Alexandra I., Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi, and Denis N. Butusov. "Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning." Remote Sensing 12, no. 20 (October 18, 2020): 3420. http://dx.doi.org/10.3390/rs12203420.
Повний текст джерелаАнотація:
This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation.
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万, 贝. "Review of Solar Photovoltaic Power Generation Forecasting." Journal of Sensor Technology and Application 09, no. 01 (2021): 1–6. http://dx.doi.org/10.12677/jsta.2021.91001.
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Sreenivasulu, J., Sudha Dukkipati, A. V. G. A. Marthanda, and A. Pandian. "Forecasting of photovoltaic power using probabilistic approach." Materials Today: Proceedings 45 (2021): 6800–6803. http://dx.doi.org/10.1016/j.matpr.2020.12.910.
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Russo, M., G. Leotta, P. M. Pugliatti, and G. Gigliucci. "Genetic programming for photovoltaic plant output forecasting." Solar Energy 105 (July 2014): 264–73. http://dx.doi.org/10.1016/j.solener.2014.02.021.
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Wang, Kejun, Xiaoxia Qi, and Hongda Liu. "Photovoltaic power forecasting based LSTM-Convolutional Network." Energy 189 (December 2019): 116225. http://dx.doi.org/10.1016/j.energy.2019.116225.
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Dong, Changgui, Benjamin Sigrin, and Gregory Brinkman. "Forecasting residential solar photovoltaic deployment in California." Technological Forecasting and Social Change 117 (April 2017): 251–65. http://dx.doi.org/10.1016/j.techfore.2016.11.021.
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Ohtake, Hideaki, Takahiro Takamatsu, and Takashi Oozeki. "A Review on Photovoltaic Power Forecasting Technics." IEEJ Transactions on Power and Energy 142, no. 11 (November 1, 2022): 533–41. http://dx.doi.org/10.1541/ieejpes.142.533.
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Sobri, Sobrina, Sam Koohi-Kamali, and Nasrudin Abd Rahim. "Solar photovoltaic generation forecasting methods: A review." Energy Conversion and Management 156 (January 2018): 459–97. http://dx.doi.org/10.1016/j.enconman.2017.11.019.
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Khalil, Ihsan Ullah, Azhar ul Haq, and Naeem ul Islam. "A novel procedure for photovoltaic fault forecasting." Electric Power Systems Research 226 (January 2024): 109881. http://dx.doi.org/10.1016/j.epsr.2023.109881.
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KORAB, Roman. "Short-term forecasting of photovoltaic power generation." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 9 (September 28, 2023): 33–38. http://dx.doi.org/10.15199/48.2023.09.06.
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Mohamad Radzi, Putri Nor Liyana, Muhammad Naveed Akhter, Saad Mekhilef, and Noraisyah Mohamed Shah. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting." Sustainability 15, no. 4 (February 6, 2023): 2942. http://dx.doi.org/10.3390/su15042942.
Повний текст джерелаАнотація:
Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable’s technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively.
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Jung, A.-Hyun, Dong-Hyun Lee, Jin-Young Kim, Chang Ki Kim, Hyun-Goo Kim, and Yung-Seop Lee. "Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea." Energies 15, no. 21 (October 23, 2022): 7853. http://dx.doi.org/10.3390/en15217853.
Повний текст джерелаАнотація:
Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management.
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Stoliarov, Oleksandr. "Efficient electricity generation forecasting from solar power plants using technology: Integration, benefits and prospects." Вісник Черкаського державного технологічного університету 29, no. 1 (February 17, 2024): 73–85. http://dx.doi.org/10.62660/bcstu/1.2024.73.
Повний текст джерелаАнотація:
Accurate prediction of electricity generation from renewable sources is an essential element to ensure the stability of electricity systems and the transition to more sustainable energy production. The study aims to optimise the operation of Ukrainian power systems through the introduction of the required share of renewable energy sources to ensure the reliability of the power system. To study the accuracy of forecasting electricity generation by photovoltaic power plants in Ukraine, data analysis, a review of existing forecasting models and methods, and comparative analysis using satellite images and meteorological observations were used. Low accuracy of forecasting output is a feature of electricity generation from renewable energy sources, which is explained by the random nature of energy sources and related meteorological conditions. In Ukraine, the problem of qualitative forecasting of electricity generation from renewable sources is becoming more relevant. The importance of finding effective methods for forecasting electricity generation in Ukraine has increased with the emergence of the electricity market. This study addresses the issue of forecasting electricity generation by photovoltaic power plants for the day ahead in the conditions of the Ukrainian energy market. As part of the study, the issues of Ukrainian legislation regarding the requirements for the accuracy of electricity generation forecasting and the consequences of their failure were considered. The study also reviewed modern models and methods for forecasting electricity generation by photovoltaic power plants and explored the new “forecasting system market” in Ukraine. The study presents accepted forecasting metrics that allow estimating errors and comparing the effectiveness of different forecasting methods. Considering the dependence of electricity generation forecasting on meteorological parameters, a comparative analysis of forecasting accuracy using satellite images and meteorological observations was carried out. The study will determine the material presented in determining the model for forecasting electricity generation, thus increasing the efficiency of energy companies in the conditions of the Ukrainian energy market. The study will also reduce the negative impact of the energy sector on the environment and contribute to a more efficient and stable electricity system in the future
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Yamamoto, Hiroki, Junji Kondoh, and Daisuke Kodaira. "Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation." Energies 15, no. 15 (July 22, 2022): 5337. http://dx.doi.org/10.3390/en15155337.
Повний текст джерелаАнотація:
Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation (LUBE) using neural networks with two outputs to make probabilistic modeling for predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), continuous ranked probability score (CRPS), and loss, which is the integration of PICP and MPIW, by removing unnecessary features through feature selection. When features with high gain were selected by random forest (RF), in the modeling of 14.7 kW PV systems, loss improved by 1.57 kW, CRPS by 0.03 kW, PICP by 0.057 kW, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise and reduce the modeling accuracy.
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Jogunuri, Sravankumar, F. T. Josh, J. Jency Joseph, R. Meenal, R. Mohan Das, and S. Kannadhasan. "Forecasting hourly short-term solar photovoltaic power using machine learning models." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 4 (December 1, 2024): 2553. http://dx.doi.org/10.11591/ijpeds.v15.i4.pp2553-2569.
Повний текст джерелаАнотація:
Forecasting solar photovoltaic power ensures a stable and dependable power grid. Given its dependence on stochastic weather conditions, predicting solar photovoltaic power accurately demands applying intelligent and sophisticated techniques capable of handling its inherent nonlinearity and volatility. Controlling electrical energy sources is an important strategy for reaching this energy balance because grid operators often have no control over use patterns. Accurately forecasting photovoltaic (PV) power generation from highly integrated solar plants to the grid is essential for grid stability. This study aims to improve forecasting accuracy and make accurate predictions of solar power output from the selected grid-connected PV system. In this study, the weather data was collected on-site and recorded PV power from a 20 kW on-grid system for one year, and different machine learning techniques like deep neural networks, random forests, and artificial neural networks were evaluated and benchmarked against reference support vector regression model. With improvements in forecasting accuracy of 2 to 37% over the reference model at study location (22.780 N, 73.650 E), College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, India, simulation results showed that the random forest technique is effective for the forecasting horizons of 1 to 4 hours.
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Castillo-Rojas, Wilson, Juan Bekios-Calfa, and César Hernández. "Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks." International Journal of Photoenergy 2023 (April 18, 2023): 1–19. http://dx.doi.org/10.1155/2023/2592405.
Повний текст джерелаАнотація:
In recent years, photovoltaic energy has become one of the most implemented electricity generation options to help reduce environmental pollution suffered by the planet. Accuracy in this photovoltaic energy forecasting is essential to increase the amount of renewable energy that can be introduced to existing electrical grid systems. The objective of this work is based on developing various computational models capable of making short-term forecasting about the generation of photovoltaic energy that is generated in a solar plant. For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. RNN models have a particular configuration that makes them efficient for processing ordered data in time series. The results of this work have been obtained through controlled experiments with different configurations of its hyperparameters for hybrid RNN-ANN models. From these, the three models with the best performance are selected, and after a comparative analysis between them, the forecasting of photovoltaic energy production for the next few hours can be determined with a determination coefficient of 0.97 and root mean square error (RMSE) of 0.17. It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.
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Yang, Huixuan, Ming Su, Xin Li, Ruizhao Zhang, and Jinhui Liu. "Distributed Energy Grid-Connected Dense Data Forecasting Technology Based on Federated Learning." Journal of Physics: Conference Series 2592, no. 1 (September 1, 2023): 012013. http://dx.doi.org/10.1088/1742-6596/2592/1/012013.
Повний текст джерелаАнотація:
Abstract Photovoltaic power generation system is one of the main clean energy power generation systems at present, which plays an important role in daily production and life. However, the photovoltaic power generation system is easily affected by various factors, and the output power will be unstable in the practical application process, which will affect the power generation efficiency. In this paper, a prediction method of distributed energy grid-connected dense data based on federated learning is constructed. This method can not only realize the short-term prediction of distributed photovoltaic power generation data, but also ensure that the data can be encrypted and modeled, thus solving the “digital island” problem. The model evaluation shows that the method in this paper performs well in short-term photovoltaic power generation prediction, and it can predict the short-term power generation of different photovoltaic power stations with high prediction accuracy. This method is of great significance to improve the management and scheduling ability and energy utilization rate of distributed photovoltaic power generation systems.
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Serrano Ardila, Vanessa María, Joylan Nunes Maciel, Jorge Javier Gimenez Ledesma, and Oswaldo Hideo Ando Junior. "Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting." Energies 15, no. 3 (January 24, 2022): 845. http://dx.doi.org/10.3390/en15030845.
Повний текст джерелаАнотація:
Solar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two FTS methods are proposed and evaluated to obtain a global horizontal irradiance (GHI) value. The first is the weighted method and the second is the fuzzy information granular method. Using the direct proportionality of the power with the GHI, the spatial smoothing process was applied, obtaining spatial irradiance on which a first-order low pass filter was applied to simulated power photovoltaic system generation. Thus, this study proposed indirect and direct forecasting of solar photovoltaic generation which was statistically evaluated and the results showed that the indirect prediction showed better performance with GHI than the power simulation. Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting.