scholarly journals The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Abdelhakim El hendouzi ◽  
Abdennaser Bourouhou ◽  
Omar Ansari
Keyword(s):  
Absolute Error ◽  
Skill Score ◽  
Weather Data ◽  
Short Term ◽  
Pv System ◽  
Pv Systems ◽  
Forecasting Models ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Power Forecasting

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.

PV System Power Forecasting Based on Neural Network with Fuzzy Processing of Weather Factors

2013 ◽  
Vol 860-863 ◽  
pp. 172-175
Author(s):  
Yong Qiang Hu ◽  
Ming Yu Wang
Keyword(s):  
Neural Network ◽  
Weather Data ◽  
Membership Functions ◽  
Short Term ◽  
Weather Factors ◽  
Pv System ◽  
Forecasting Method ◽  
Temperature Radiation ◽  
System Power ◽  
Power Forecasting

A short-term PV system power forecasting method is presented in the paper based on neural network considering fuzzy characteristics of weather factors. Weather factors that affect PV system power output mainly include temperature, radiation intensity, rain and relative humidity which are all of strong fuzziness. The paper firstly made use of membership functions to process their fuzziness. Then, the historical power data of a PV system was put into neural network together with fuzzy processed historical weather data to train the network, therefore, neural network that be able to forecast PV power was get. Finally, data of an actual PV system in Colorado was employed to methods with and without fuzzy processing of weather factors, results show that the method with fuzzy processing is more accurate than that without fuzzy processing.

scholarly journals Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4733
Author(s):  
Andi A. H. Lateko ◽  
Hong-Tzer Yang ◽  
Chao-Ming Huang ◽  
Happy Aprillia ◽  
Che-Yuan Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Weather Data ◽  
Support Vector ◽  
Short Term ◽  
Learner Model ◽  
Average Maximum ◽  
Power Forecasting

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

Error analysis of hybrid photovoltaic power forecasting models: A case study of mediterranean climate

2015 ◽  
Vol 100 ◽  
pp. 117-130 ◽  
Author(s):  
Maria Grazia De Giorgi ◽  
Paolo Maria Congedo ◽  
Maria Malvoni ◽  
Domenico Laforgia
Keyword(s):  
Error Analysis ◽  
Mediterranean Climate ◽  
Forecasting Models ◽  
Photovoltaic Power ◽  
Power Forecasting

scholarly journals Very short-term photovoltaic power forecasting with cloud modeling: A review

2017 ◽  
Vol 75 ◽  
pp. 242-263 ◽  
Author(s):  
Florian Barbieri ◽  
Sumedha Rajakaruna ◽  
Arindam Ghosh
Keyword(s):  
Short Term ◽  
Photovoltaic Power ◽  
Cloud Modeling ◽  
Power Forecasting

scholarly journals Weather data errors analysis in solar power stations generation forecasting

2018 ◽  
Vol 51 ◽  
pp. 02002 ◽  
Author(s):  
Stanislav Eroshenko ◽  
Alexandra Khalyasmaa
Keyword(s):  
Detailed Analysis ◽  
Solar Power ◽  
Weather Data ◽  
Forecasting Model ◽  
Short Term ◽  
Data Errors ◽  
Power Stations ◽  
Software Product ◽  
Short Term Forecasting ◽  
Errors Analysis

The paper presents a short-term forecasting model for solar power stations (SPS) generation developed by the authors. This model is based on weather data and built into the existing software product as a separate short-term forecasting module for the SPS generation. The main problems associated with forecasting the SPS generation on cloudy days were revealed in the framework of authors' research, which is due not to the error of the developed model but to the use of the same learning sample for both solar and cloudy days. This paper contains analysis of the main problems related to the learning sampling, samples pattern, quality and representativeness for forecasting the SPS generation on cloudy days. Besides, the paper includes a calculation example performed for the existing SPS and a detailed analysis of the forecast generation on cloudy days based on the actual weather provider data.

scholarly journals Comparison Between Measurements and WRF Numerical Simulation of Global Solar Irradiation in Romania

2013 ◽  
Vol 57 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Dragos Isvoranu ◽  
Viorel Badescu
Keyword(s):  
Numerical Simulation ◽  
Preliminary Analysis ◽  
Measured Data ◽  
Solar Irradiation ◽  
Short Term ◽  
Data Accessibility ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Global Solar Irradiation ◽  
Global Irradiation

Abstract The paper presents a comparative analysis between the surface global irradiation measured for Romania and the predicted irradiation obtained by numerical simulation. The measured data came from the Romanian National meteorological Administration. Based on a preliminary analysis that took into account several criteria among which, performance, cost, popularity and meteorological and satellite data accessibility we concluded that a combination GFS-WRF(NMM) or GFS-WRF(ARW) is most suitable for short term global solar irradiation forecasting in order to assess the performance of the photovoltaic power stations (Badescu and Dumitrescu, 2012, [1], Martin et al., 2011, [2]).

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