Multiple Effect of Climatic Factors and Elements on Photovoltaic Generation in On-Grid System using Conventional Station Data

l i ma t i ce l e me n t st h a ts h o w ah i g hc o r r e l a t i o ni sn e e d e d .Ke y wo r d s:기후요소( C l i ma t i ce l e me n t ) ,건물일체형 태양광발전( B u i l d i n gi n t e g r a t e dp h o t ov o l t a i c ) ,체육관( Gy mn a s i u m b u i l d i n g ) ,상관계수( C o e f f i c i e n to fc o r r e l a t i o n ) ,운량( C l o u dc o v e r ) 1 .서론 전 세계적인 경제성장과 중국과 같은 신흥

Most air pollution and smog are a result of the burning of fossil fuels. The use of fossil fuels also causes acid rain and global warming. So the need for solar energy utilization is increased. It is essentially important to make efforts to reduce usage of fossil energy resources. In this study, we analyzed the correlation between climatic elements(Cloud cover, Duration of sunshine, Temperature) and the photovoltaic power generation. Cloud cover of the correlation coefficient was 0.87. And duration of sunshine of the correlation coefficient was 0.93. The order of the correlation coefficient was duration of sunshine, cloud cover, temperature. To accurately analyze of the degree of correlation for the photovoltaic power generation, additional research about climatic elements that show a high correlation is needed.

Photovoltaic cell generation is the technique which uses photovoltaic cell to convert solar energy into electrical energy. Now a days ,the photovoltaic generation is developing increasingly fast as a renewable energy source. The functioning of a photovoltaic cell as the power generator is equivalent to an electric circuit containing a current generator, diode, series resistance and shunt resistance. This paper presents a modeling and simulation of a photovoltaic system constitutes of a generator (PVG), DC-DC converter (boost chopper) to transfer the maximum power to a base transmitter station. The temperature and irradiance effects on the PVG will be studied, particularly on the variables such as the short circuit current Icc, the open circuit voltage Voc, the performance η and the fill factor FF. Depending on the load (BTS, I=60A, V=48V) profile and climatic factors influencing, we can find a highly gap between the maximum power supplied by the PVG and that actually transferred to the BTS. A maximum power point tracker (MPPT) based on a boost converter commanded by a Pulse Width Modulation (PWM) is used for extracting the maximum power from the PVG. Thus, a real time tracking of the optimal point of functioning (MPP: Maximum Power Point) is necessary to optimize the efficiency on the system. The modeling and simulation of the system (PVG, boost converter, PWM and MPPT algorithm Perturbation and Observation P&O) is then made with Matlab/Simulink software. DOI: http://dx.doi.org/10.11591/ijece.v3i5.3639

The precise forecasting of photovoltaic energy generation holds paramount importance in refining scheduling and ensuring safe operation of extensive photovoltaic power stations. However, the inherent instability and volatility of photovoltaic power generation pose significant challenges to prediction accuracy. To address this, this article conducts a thorough analysis of the seasonal characteristics of photovoltaic power generation and introduces a hybrid prediction model based on the ensemble empirical mode decomposition (EEMD)-improved whale optimization algorithm (IWOA)-bidirectional long short-term memory network (BiLSTM) algorithm. This model leverages multi-seasonal meteorological features to enhance forecasting accuracy. First, EEMD is used to decompose and reconstruct photovoltaic power generation data to eliminate its instability and volatility. Second, three improved strategies are proposed for the position update in different stages of the IWOA, and a multi-seasonal prediction model based on IWOA-optimized Bidirectional LSTM is established. Finally, the operational data of a photovoltaic power station in the northwest region of China are used as a case study to evaluate the prediction performance of the model in detail. The results show that the model's accuracy rate ranges from 97.1% to 98.7%, which can accurately predict photovoltaic power generation and improve the utilization rate of renewable energy.

With the popularization of solar energy development and utilization, photovoltaic power generation is widely used in countries around the world and is increasingly becoming an important part of new energy generation. However, it cannot be ignored that changes in solar radiation and meteorological conditions can cause volatility and intermittency in power generation, which, in turn, affects the stability and security of the power grid. Therefore, many studies aim to solve this problem by constructing accurate power prediction models for PV plants. However, most studies focus on adjusting the photovoltaic power station prediction model structure and parameters to achieve a high prediction accuracy. Few studies have examined how the various parameters affect the output of photovoltaic power plants, as well as how significantly and effectively these elements influence the forecast accuracy. In this study, we evaluate the correlations between solar irradiance intensity (GHI), atmospheric density (ρ), cloudiness (CC), wind speed (WS), relative humidity (RH), and ambient temperature (T) and a photovoltaic power station using a Pearson correlation analysis and remove the factors that have little correlation. The direct and indirect effects of the five factors other than wind speed (CC) on the photovoltaic power station are then estimated based on structural equation modeling; the indirect effects are generated by the interaction between the variables and ultimately have an impact on the power of the photovoltaic power station. Particle swarm optimization-based support vector regression (PSO-SVR) and variable weights utilizing the Mahalanobis distance were used to estimate the power of the photovoltaic power station over a short period of time, based on the contribution of the various solar radiation and climatic elements. Experiments were conducted on the basis of the measured data from a distributed photovoltaic power station in Changzhou, Jiangsu province, China. The results demonstrate that the short-term power of a photovoltaic power station is significantly influenced by the global horizontal irradiance (GHI), ambient temperature (T), and atmospheric density (ρ). Furthermore, the results also demonstrate how calculating the relative importance of the various contributing factors can help to improve the accuracy when estimating how powerful a photovoltaic power station will be. The multiple weighted regression model described in this study is demonstrated to be superior to the standard multiple regression model (PSO-SVR). The multiple weighted regression model resulted in a 7.2% increase in R2, a 10.7% decrease in the sum of squared error (SSE), a 2.2% decrease in the root mean square error (RMSE), and a 2.06% decrease in the continuous ranked probability score (CRPS).

The objective of this review was to evaluate, through research works found in the literature, the influence of climatic elements on production animals in tropical climates. The effects and influence of climatic factors and elements on physiological behavior, hematological parameters, weight gain, thyroid hormone profile, electrolytes and milk production in a tropical climate were studied. Climatic elements significantly interfere with the productive, reproductive and adaptive parameters of animals raised in the Brazilian semi-arid region. Concluding with this study, it is necessary to use techni-ques that reduce the impact of bad weather so that animals can express their productive potential. Furthermore, it is worth mentioning that it is not enough to focus on environmental factors, as these are not the only ones that contribute to the process of producing meat, milk, skin or wool. It is necessary to pay attention to the nutritional, health and management factors of each species created.

Statistics concerning the budgets of angular momentum heat and water vapor over the Northern Hemisphere are computed by two different methods for the winter of 1976–77. The first method employs an objective analysis scheme applied to the set of conventional upper air sounding obtained from the hemispheric network of rawinsonde stations. The second method uses grid-point values produced daily by the NMC global Hough analysis based on data from several sources. Our results show that the gridded Hough data do not contain mean meridional circulations, thus seriously limiting their usefulness for studies in which these cells play a major role. In addition, the gridded data appear to yield unreasonably large values of water vapor. On the other hand, they produce a realistic temperature structure and seem quite adequate for use in studies of midlatitude waves and their transports. They have also proven much easier to work with than the conventional station data. We find, too, that these station data have their own deficiencies caused largely by gaps in the rawinsonde network, such as those resulting from the loss of several ocean weather ship stations since 1973. Our study also provides an added appreciation for the highly amplified nature of atmospheric waves during the 1976–77 winter. A strong conversion of kinetic energy from its eddy to zonal mean state and a large standing eddy heat flux are both evident. Additionally, transient eddy momentum fluxes were found to peak at 230 mb, a level not usually included in previous general circulation statistics.

Multi-dimensional climate factors like irradiation and humidity will lead to strong randomness of photovoltaic power generation, and it is difficult to consider all climate factors in traditional photovoltaic power generation point forecasting. Vine copula can accurately and flexibly describe the dependencies between multi-dimensional variables, establish conditional distribution expressions between photovoltaic power generation and multi-dimensional climate factors, and improve prediction accuracy. This paper proposes a D-vine copula model under typical climatic conditions. The model uses fuzzy C-means (FCM) to complete typical climate clustering, and then proposes a D-vine copula model to achieve point predictions and probability interval predictions. The results of the example show that the model has a good performance in point predictions and provide corresponding probability interval predictions, which prove the accuracy and applicability of the model.

Photovoltaic power generation has great potential to replace the traditional coal-fired power generation which has high pollution and high energy consumption. However, photovoltaic power generation has great uncertainty, so how to accurately predict the photovoltaic power generation is an urgent issue to be solved. Currently, Artificial Intelligence provides a promising way to improve prediction accuracy and reliability. In addition, compared with deterministic prediction, probabilistic prediction can analyze the probability distribution of photovoltaic power generation at a certain time period to obtain more favorable results. In this paper, based on a deep learning algorithm, a probabilistic algorithm of time series prediction based on Bayesian optimization is proposed. The input data are optimized by the Bayesian networks, and deterministic results are predicted by using the LSTM network. A case study of prediction results shows that the probabilistic prediction interval of photovoltaic power under 15%-95% incredible conditions can be effectively obtained. The actual operation data of the photovoltaic power station are used to verify and the prediction results show that the precision of the optimized data is increased by about 20%, which shows good engineering application

An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation

The aim of this paper is to validate the data of three meteorological elements Air Temperature (Ta) , Relative Humidity (RH), Wind Speed (WS) from the European Center For-Medium Range Weather Forecasting (ECMWF) , against ground stations data using several Models at six stations well distributed in Iraq (Mosul, Kirkuk, Baghdad, Kut, Nasiriya, and Basra).
 Due to the difficulties which experienced by the ground climate stations in Iraq from a shortage of devices and equipment for measuring the various climatic elements, which led to a huge shortage of data throughout time for political, economic and natural disasters. It is found that researchers can adopt the data of satellite stations to monitor the climate because let’s found that there is a highly significance Correlations between the data of these stations and the data of the ground stations for climate monitoring
 Five Mathematical Models were used for that [Linear Models, Quadratic Models, Exponential Models, Logarithmic Models, and Power Models]. The performance of these models were evaluated by comparing the calculated (Ta, RH, WS) from earth stations.
 Those mathematical correlations help to be able to calculate the ground data in state of there is no ground climate stations data.
 Several statistical tests Correlation Coefficient (R), Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) were used to control the validation and goodness of these Models.
 The R2 obtained from these Models were very high in all stations. This means that, there is a highly significance Correlations between (Ta, RH, WS) estimated and [Ta, RH, WS] measured in all station

Analysis of output coupling characteristics among multiple photovoltaic power stations based on correlation coefficient

In order to remedy problems encompassing large-scale data being collected by photovoltaic (PV) stations, multiple dimensions of power prediction mode input, noise, slow model convergence speed, and poor precision, a power prediction model that combines the Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) with Long Short-Term Memory (LSTM) network was proposed in this study. The corresponding model uses factor correlation coefficient to evaluate the factors that affect PV generation and obtains the most critical factor of PV generation. Then, it uses CCIPCA to reduce the dimension of PV super large-scale data to the factor dimension, avoiding the complex calculation of covariance matrix of algorithms such as Principal Component Analysis (PCA) and to some extent eliminating the influence of noise made by PV generation data acquisition equipment and transmission equipment such as sensors. The training speed and convergence speed of LSTM are improved by the dimension-reduced data. The PV generation data of a certain power station over a period is collected from SolarGIS as sample data. The model is compared with Markov chain power generation prediction model and GA-BP power generation prediction model. The experimental results indicate that the generation prediction error of the model is less than 3%.

Speed Sensorless Control with a Linearization by State Feedback of Induction Machine with Adaptation of the Rotor Time-constant Using Fuzzy Regulator Powered by Photovoltaic Solar Energy

Solar energy is one of the most widely used renewable energy sources in the world and its development and utilization are being integrated into people’s lives. Therefore, accurate solar radiation data are of great significance for site-selection of photovoltaic (PV) power generation, design of solar furnaces and energy-efficient buildings. Practically, it is challenging to get accurate solar radiation data because of scarce and uneven distribution of ground-based observation sites throughout the country. Many artificial neural network (ANN) estimation models are therefore developed to estimate solar radiation, but the existing ANN models are mostly based on conventional meteorological data; clouds, aerosols, and water vapor are rarely considered because of a lack of instrumental observations at the conventional meteorological stations. Based on clouds, aerosols, and precipitable water-vapor data from Moderate Resolution Imaging Spectroradiometer (MODIS), along with conventional meteorological data, back-propagation (BP) neural network method was developed in this work with Levenberg-Marquardt (LM) algorithm (referred to as LM-BP) to simulate monthly-mean daily global solar radiation (M-GSR). Comparisons were carried out among three M-GSR estimates, including the one presented in this study, the multiple linear regression (MLR) model, and remotely-sensed radiation products by Cloud and the Earth’s radiation energy system (CERES). The validation results indicate that the accuracy of the ANN model is better than that of the MLR model and CERES radiation products, with a root mean squared error (RMSE) of 1.34 MJ·m−2 (ANN), 2.46 MJ·m−2 (MLR), 2.11 MJ·m−2 (CERES), respectively. Finally, according to the established ANN-based method, the M-GSR of 36 conventional meteorological stations for 12 months was estimated in 2012 in the study area. Solar radiation data based on the LM-BP method of this study can provide some reference for the utilization of solar and heat energy.