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In the past decade, solar energy occupies an increasing share of the power grid. The intermittent fluctuations characteristics of solar irradiance bring great difficulties and challenges to the power system management and dispatch which simultaneously makes photovoltaic power forecast the most important. For this reason, pattern recognition model for weather statuses is constructed, classification...
There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model...
In order to improve the prediction accuracy of the photovoltaic power prediction model, this paper presents a novel forecasting model, which is the combination between Principal Component Analysis (PCA) and Support Vector Machine (SVM) intelligent algorithm. PCA statistical method used to extract less principal components instead of the original meteorological factor. Then that is as the input of...
in this study, three models namely the linear coefficient model, the Standard ASTM E2527 and the Sandia National Laboratories model, among some methods of the prediction of maximum power have been adequately selected to investigate the performance of a High concentrator photovoltaic (HCPV) system, upon atmospheric conditions such as the direct normal irradiation DNI, the ambient temperature, the wind...
In this work, an operational solar energy forecast model was developed based in correction of irradatiation obtained from WRF output. This output depends on the Hourly Clearness Index (kt). Three different physic schemes of WRF parametrization are analyzed in this work. The horizontal irradiation computed by Weather Research and Forecasting (WRF) is the input of the developed Model Output Statistic...
To solve the problem of the variance of the photovoltaic power when photovoltaic power station connect with the power grid, a photovoltaic power predicting model of photovoltaic power station based on double ANNs is proposed in the paper. Wind velocity and wind direction on photovoltaic power station are the key of photovoltaic power predicting, and other circumstance conditions such as temperature,...
Performance models for photovoltaic modules are traditionally calibrated from measurements of module output under carefully controlled indoor or outdoor conditions. Recently Sandia National Laboratories has published methods to obtain performance model parameters from measured output of modules outdoors on fixed tilt racks. Here we investigate the dependence of a model's prediction accuracy on the...
PV modules and their inverting electronics are becoming increasingly more integrated. When a PV module and microinverter are completely integrated a new class of PV system, the AC module, is created. Unfortunately, existing characterization and modeling techniques require separate characterization of PV and inverting components. Thus, existing methods are incapable of modeling AC modules. We have...
Short-term forecasting of the energy production is one of the key issues in smart homes that tend to achieve efficient balance among the energy production, storage and consumption. In this paper, we first perform an analysis of the features to be used by the most promising short-term forecast model: artificial neural networks. We determine the best performing offline model and then propose an online...
Predicting of solar resource in general is momentous for preparation of the operations of power plants which transform renewable energies to electricity. In particular, the possibility to predict the solar irradiance (up to one day or even more) can become of significant interest with reference to Grid connected Photovoltaic (PV) Plants, stand alone and hybrid system. In this paper, a MATLAB/SIMULINK...
Seasonal and short-term weather-related changes in the solar spectrum can induce shifts in the performance of photovoltaic (PV) systems that affect both annual energy predictions and system characterization. The spectral shift factor, which is a metric indicative of how much the performance of a PV system will vary from nameplate due to deviations from the ASTM G173 spectrum (air mass of 1.5), is...
Solar energy has emerged as a renewable, clean, reliable, and free source of energy encapsulated in photovoltaic (PV) cells. Studying the factors and parameters that affect the performance of these cells is significantly helping researchers to understand, design, develop, and optimize them. It has been reported that PV cell performance is highly affected by operating temperature. PV module temperature...
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