scholarly journals A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yuan-Kang Wu ◽  
Chao-Rong Chen ◽  
Hasimah Abdul Rahman
Keyword(s):  
Power Output ◽  
Solar Power ◽  
Peak Load ◽  
Weather Conditions ◽  
Green Energy ◽  
Weather Data ◽  
Stable Operation ◽  
Short Term ◽  
Pv System ◽  
Experimental Database

The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

scholarly journals An Efficient Intelligent Power Detection Method for Photovoltaic System

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Detection Method ◽  
Peak Load ◽  
Weather Conditions ◽  
Photovoltaic System ◽  
Weather Data ◽  
Weather Factors ◽  
Pv System ◽  
Intelligent Technique ◽  
Highly Nonlinear ◽  
Validation Set

Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

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 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.

Electric Vehicle as an Energy Storage for Grid Connected Solar Power System

2015 ◽  
Vol 6 (3) ◽  
pp. 567 ◽  
Author(s):  
E. Sheeba Percis ◽  
Manivannan S ◽  
Nalini A
Keyword(s):  
Energy Storage ◽  
Electric Vehicle ◽  
Solar Irradiance ◽  
Solar Power ◽  
Weather Conditions ◽  
Real Field ◽  
Technological Advancement ◽  
Atmospheric Conditions ◽  
Pv System ◽  
Point Tracking

In the past few years the growing demand for electricity and serious concern for the environment have given rise to the growth of sustainable sources like wind, solar, tidal, biomass etc. The technological advancement in power electronics has led to the extensive usage of solar power. Solar power output varies with the weather conditions and under shading conditions. With the increasing concerns of the impacts of the high penetration of Photovoltaic (PV) systems, a technical study about their effects on the power quality of the utility grid is required. This paper investigates the functioning of a grid-tied PV system along with maximum power point tracking (MPPT) algorithm. The effects of varying atmospheric conditions like solar irradiance and temperature are also taken into account. It is proposed in this work that an Electric Vehicle (EV) can be used as an energy storage to stabilize the power supplied to the grid from the photovoltaic resources. A coordinated control is necessary for the EV to obtain desired outcome. The modeling of the PV and EV system is carried out in PSCAD and the proposed idea is verified through simulation results utilizing real field data for solar irradiance and temperature.

scholarly journals Solar Power Plant Generation Short-Term Forecasting Model

2018 ◽  
Vol 208 ◽  
pp. 04004
Author(s):  
Stanislav Eroshenko ◽  
Elena Kochneva ◽  
Pavel Kruchkov ◽  
Aleksandra Khalyasmaa
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Solar Power ◽  
Weather Conditions ◽  
Solar Power Plant ◽  
Short Term ◽  
Variable Capacity ◽  
Solar Power Plants ◽  
Rapid Pace ◽  
Short Term Forecasting

Recently, renewable generation plays an increasingly important role in the energy balance. Solar energy is developing at a rapid pace, while the solar power plants output depends on weather conditions. Solar power plant output short-term forecasting is an urgent issue. The future electricity generation qualitative forecasts allow electricity producers and network operators to actively manage the variable capacity of solar power plants, and thereby to optimally integrate the solar resources into the country's overall power system. The article presents one of the possible approaches to the solution of the short-term forecasting problem of a solar power plant output.

scholarly journals An improved MPPT control strategy based on incremental conductance algorithm

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Liqun Shang ◽  
Hangchen Guo ◽  
Weiwei Zhu
Keyword(s):  
Control Strategy ◽  
External Environment ◽  
Weather Conditions ◽  
Response Speed ◽  
Step Response ◽  
Stable Operation ◽  
Pv System ◽  
Radiation Level ◽  
Incremental Conductance ◽  
Incremental Conductance Algorithm

Abstract PV power production is highly dependent on environmental and weather conditions, such as solar irradiance and ambient temperature. Because of the single control condition and any change in the external environment, the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate, resulting in misjudgment. To improve the efficiency and economy of PV systems, an improved incremental conductance algorithm of MPPT control strategy is proposed. From the traditional incremental conductance algorithm, this algorithm is simple in structure and can discriminate the instantaneous increment of current, voltage and power when the external environment changes, and so can improve tracking efficiency. MATLAB simulations are carried out under rapidly changing solar radiation level, and the results of the improved and conventional incremental conductance algorithm are compared. The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence. It not only optimizes the system, but also improves the efficiency, response speed and tracking efficiency of the PV system, thus ensuring the stable operation of the power grid.

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.

scholarly journals A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Deng Yongsheng ◽  
Jiao Fengshun ◽  
Zhang Jie ◽  
Li Zhikeng
Keyword(s):  
Correlation Analysis ◽  
Power Output ◽  
Short Term Memory ◽  
Rank Order ◽  
Influence Factors ◽  
Forecasting Model ◽  
Short Term ◽  
Pv System ◽  
Factors Affecting ◽  
Model Based

Accurate short-term power output forecasting results are conducive to reducing the scheduling difficulty of grid-connected operation of distributed photovoltaic (PV) systems, thus improving the safety and stability of power grid operation. In this paper, a one-day-ahead short-term power output forecasting model based on correlation analysis and combination algorithms for distributed PV system is proposed to solve the problems within the current methods. Firstly, the basic information of distributed PV system is introduced, and the main influence factors affecting the power output of distributed PV system are determined. Secondly, the influence factors with higher correlation with PV output are selected by Spearman rank-order correlation coefficient (SROCC) analysis in multiple timescales. Then, based on the multimodel univariate extreme learning machine (ELM) submodel and the single-model multivariate long short-term memory (LSTM) submodel, the ELM-LSTM model is established. The case study analysis based on the actual data indicates that the ELM-LSTM forecasting model proposed in this paper has higher forecasting accuracy than the traditional forecasting methods.

Prediction of Photovoltaic Power Output Considering Weather Conditions

Solar Energy ◽  
2006 ◽  
Author(s):  
Kaoru Furushima ◽  
Yutaka Nawata ◽  
Michio Sadatomi
Keyword(s):  
Heat Transfer ◽  
Silicon Solar Cell ◽  
Weather Conditions ◽  
Weather Data ◽  
Cell Temperature ◽  
Pv System ◽  
Voltage Current Characteristic ◽  
Current Characteristics ◽  
Modified Equation ◽  
Electrical Output

A reasonable construction of photovoltaic (PV) system would be possible if the electrical output from the PV can be predicted accurately from weather data before the construction. The electrical output can be calculated theoretically from the voltage-current characteristics equation for a silicon solar cell, if the solar irradiance and the cell temperature are known. However, it is not easy to predict the cell temperature because it depends on several physical and environment factors; in particular, it is difficult to estimate a heat transfer from PV surface to surrounds. In this study, firstly, we confirmed that the electrical output can be predicted accurately by a modified equation of traditional voltage-current characteristic equation when the irradiance and the cell temperature are given. Secondly, we estimated the average heat transfer coefficient from PV surface as a function of wind speed using the experimental data on the PV system obtained in this study and compared it with three kinds of data in references. As a result, the estimated values agree with the references’ results in their accuracy of the data.

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