scholarly journals The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation

2020 ◽  
Vol 10 (22) ◽  
pp. 8265
Author(s):  
Stanislav A. Eroshenko ◽  
Alexandra I. Khalyasmaa ◽  
Denis A. Snegirev ◽  
Valeria V. Dubailova ◽  
Alexey M. Romanov ◽  
...  
Keyword(s):  
Power Generation ◽  
Time Domain ◽  
Power Plants ◽  
Multiple Time ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Photovoltaic Power ◽  
Operational Forecasting ◽  
Short Term Forecasting ◽  
Forecasting Errors

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.

scholarly journals Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

2020 ◽  
Vol 185 ◽  
pp. 01052
Author(s):  
Runjie Shen ◽  
Ruimin Xing ◽  
Yiying Wang ◽  
Danqiong Hua ◽  
Ming Ma
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Random Forest ◽  
Prediction Model ◽  
Power Plants ◽  
Time Series Prediction ◽  
Cloud Amount ◽  
Continuous Model ◽  
Short Term ◽  
Photovoltaic Power

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

scholarly journals Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

2016 ◽  
Vol 53 (2) ◽  
pp. 3-13 ◽  
Author(s):  
V. Radziukynas ◽  
A. Klementavičius
Keyword(s):  
Power System ◽  
Wind Power ◽  
Arima Models ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Additional Input ◽  
Input Variables ◽  
Short Term Forecasting ◽  
Performance Results ◽  
Forecasting Errors

Abstract The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

Super short term forecasting of photovoltaic power generation output in micro grid

2017 ◽  
Author(s):  
Cheng Gong ◽  
Longfei Ma ◽  
Zhongjun Chi ◽  
Baoqun Zhang ◽  
Ran Jiao ◽  
...  
Keyword(s):  
Power Generation ◽  
Short Term ◽  
Photovoltaic Power ◽  
Micro Grid ◽  
Short Term Forecasting

scholarly journals Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors

2020 ◽  
Vol 13 (2) ◽  
pp. 188-195
Author(s):  
Kriangkamon Khumma ◽  
Kreangsak Tamee
Keyword(s):  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Photovoltaic Power ◽  
Minute Period ◽  
Filtering Technique ◽  
Stochastic Forecasting ◽  
Short Term Forecasting ◽  
Persistence Model ◽  
Power Forecasting

    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.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model

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

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

scholarly journals A Study on the Improvement of Efficiency by Detection Solar Module Faults in Deteriorated Photovoltaic Power Plants

2021 ◽  
Vol 11 (2) ◽  
pp. 727 ◽  
Author(s):  
Myeong-Hwan Hwang ◽  
Young-Gon Kim ◽  
Hae-Sol Lee ◽  
Young-Dae Kim ◽  
Hyun-Rok Cha
Keyword(s):  
Power Generation ◽  
Power Plants ◽  
Detection System ◽  
Failure Detection ◽  
Solar Module ◽  
Thermal Images ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Manufacturing Technologies ◽  
Pv Power Generation

In recent years, photovoltaic (PV) power generation has attracted considerable attention as a new eco-friendly and renewable energy generation technology. With the recent development of semiconductor manufacturing technologies, PV power generation is gradually increasing. In this paper, we analyze the types of defects that form in PV power generation panels and propose a method for enhancing the productivity and efficiency of PV power stations by determining the defects of aging PV modules based on their temperature, power output, and panel images. The method proposed in the paper allows the replacement of individual panels that are experiencing a malfunction, thereby reducing the output loss of solar power generation plants. The aim is to develop a method that enables users to immediately check the type of failures among the six failure types that frequently occur in aging PV panels—namely, hotspot, panel breakage, connector breakage, busbar breakage, panel cell overheating, and diode failure—based on thermal images by using the failure detection system. By comparing the data acquired in the study with the thermal images of a PV power station, efficiency is increased by detecting solar module faults in deteriorated photovoltaic power plants.

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