Formal Analysis of Solar Power and Weather Data

2022 ◽  
pp. 81-89
Author(s):  
M. Eugenia Cornejo ◽  
Jesús Medina ◽  
Clemente Rubio-Manzano
Keyword(s):  
Solar Power ◽  
Formal Analysis ◽  
Weather 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.

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 The environment coefficients effect on I-V and P-V characteristics curves of photovoltaic cell using Matlab/Simulink

2018 ◽  
Vol 7 (4) ◽  
pp. 2651
Author(s):  
Bashar Mohammed Salih ◽  
Rasha A. Mohmeed ◽  
Mohammed Ahmed Ibrahim
Keyword(s):  
Power Plant ◽  
Solar Power ◽  
Photovoltaic Cell ◽  
Weather Data ◽  
Solar Power Plant ◽  
Irradiation Effects ◽  
Characteristic Curves ◽  
Matlab Simulink ◽  
Current Voltage ◽  
Input Variables

 Many parameters and environments conditions will affect the behavior of the photovoltaic cell. This paper investigates, theoretically the variation of each temperature and irradiation effects on the output of the photovoltaic cell characteristics. Modeling of the photovoltaic cell scheme essentially requires taking weather data (temperature and irradiance) as input variables. The photovoltaic outputs are the current, voltage and power. Though, conclude the characteristics I-V or P-V desires of these important variables. Any variation in the entries directly shows variations in outputs. The characteristic curves are obtained with the use practical readings and measurements are illustrated directly from the solar power plant in the Technical Engineering College of Mosul. The complete modeling is then computer-generated using MATLAB/Simulink software owing to its common use and its helpfulness.  

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 Forecasting approach for solar power based on weather parameters (Case study: East Kalimantan)

2021 ◽  
Vol 2106 (1) ◽  
pp. 012022
Author(s):  
P Hasanah ◽  
S A Wiradinata ◽  
M Azka
Keyword(s):  
Linear Regression ◽  
Solar Energy ◽  
Multiple Linear Regression ◽  
Solar Power ◽  
Weather Data ◽  
Climate Data ◽  
Energy Forecasting ◽  
Tropical Country ◽  
East Kalimantan ◽  
Weather Parameters

Abstract Solar Energy is the most popular among several clean energies. As a tropical country, Indonesia has big opportunity to develop solar power, particularly in East Kalimantan which spans around the equator. Solar energy generation, however, is influenced by weather parameters which give uncertain values of the amount of the captured energy. Therefore, this research is conducted to overcome the effect of weather towards solar energy. The aim of this research is to examine the model for sun power forecasting based on the data. The Artificial Neural Network (ANN) and Multiple Linear Regression have taken as the approach models to determine energy forecasting. This study used five input variables; temperature, precipitation level, humidity, wind speed, and surface pressure, while the solar radiation was taken as the output variable. Moreover, the daily solar power and weather data from East Kalimantan has been taken along the period of 27th July 2018 – 28th July 2021. The result of this study showed that the RMSE of ANN was slightly similar with the multiple linear regression methods which were calculated by 160.26 and 160.46 respectively. However, the ANN is preferable to use in the solar energy forecasting since the tendency of nonlinearity of the climate data.

scholarly journals A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

2019 ◽  
Vol 11 (5) ◽  
pp. 1501 ◽  
Author(s):  
Seul-Gi Kim ◽  
Jae-Yoon Jung ◽  
Min Sim
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Solar Power ◽  
Economic Benefits ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Weather Information ◽  
Solar Power Generation ◽  
Applied Machine Learning ◽  
Modeling Process

Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds a model that predicts the amounts of solar power generation using weather information provided by weather agencies. This study proposes a two-step modeling process that connects unannounced weather variables with announced weather forecasts. The empirical results show that this approach improves a base approach by wide margins, regardless of types of applied machine learning algorithms. The results also show that the random forest regression algorithm performs the best for this problem, achieving an R-squared value of 70.5% in the test data. The intermediate modeling process creates four variables, which are ranked with high importance in the post-analysis. The constructed model performs realistic one-day ahead predictions.

scholarly journals Design of Standalone Solar Power Plantusing System Advisor Model in Indian Context

2020 ◽  
Vol 8 (5) ◽  
pp. 3154-3158
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Solar Power ◽  
Life Time ◽  
Photovoltaic System ◽  
Weather Data ◽  
Design Parameters ◽  
Energy Yield ◽  
Tropical Country ◽  
Solar Powered

The design parameters of standalone solar power plant should be analysed for feasible power generation and utilization at any site under consideration. Solar-powered photovoltaic system provides anunpolluted energy solution to current global warming. The solar input parameters and weather data are important to configure the desired power output. A simulation is necessary for the system which provides information on input data, loss under consideration, system efficiencies and gross energy yield. Standalone system is suited for small power generation setup which is not connected to grid. India being a tropical country with major sunny days throughout the year individual power plant setup helps consumers to depend less on government supplied power. Installation at site, system degradation and life time reliability analysis is crucial for long term power optimization.Virtual simulations have been carried out on PV systems for Bangalore station using system advisor model. This paper considers various design parameters to be considered before setting up a standalone system.

scholarly journals Study on Outlet Temperature Control of External Receiver for Solar Power Tower

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 340
Author(s):  
Qiang Zhang ◽  
Kaijun Jiang ◽  
Yanqiang Kong ◽  
Jiangbo Wu ◽  
Xiaoze Du
Keyword(s):  
Temperature Control ◽  
Control Strategy ◽  
Solar Power ◽  
Heat Transfer Fluid ◽  
Weather Data ◽  
Unstable State ◽  
Inlet Temperature ◽  
Outlet Temperature ◽  
Power Load ◽  
Solar Power Tower

Due to the change of direct normal irradiance (DNI) and the change of output power load, the receiver of the solar tower is in an unstable state in the actual operation. In this paper, a 100 MW external cylindric receiver is designed and modelled. The dynamic and comprehensive model is established for the receiver, including the thermal and mechanical equations. The temperature control strategy is applied to the receiver model. The validity of the control strategy is verified by disturbance experiments, including DNI, the inlet temperature of the heat transfer fluid (HTF), and the weather data on a cloudy day. The response characteristics of the receiver are demonstrated. Its thermal lag characteristics and restraining effect on the fluctuating environment are revealed. The dangerous occasion of the receiver during operation are detected, including the overheat of the local panel, and the dissociation point of the molten salt. Both the robustness and the deficiency of the control strategy of the receiver are pointed out. The research results will contribute to the control strategy formulation of the SPT (solar power tower) station.

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