Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of having access to weather information for solar power generation prediction and find weather information that can help best predict photovoltaic power.
@cite_1 also presented a similar day-based forecasting tool for 24 hour-ahead forecasting for a small scale solar power output. Their proposed forecasting method had two components: Similar Day Detection (SDD) engine and forecasting engine. Their study found the most effective external variable as the irradiance forecasts for all geographical locations that was studied. As weather forecasts are used as model inputs, inaccuracy in those forecasts lead into inevitable solar power forecast errors.
@cite_1
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A set of three methods are proposed for forecasting amounts of global solar radiation. The forecasts are used to predict the output power of photovoltaic systems installed in power systems and control the output of other generators to meet the electricity demand. The methods forecast two-day-ahead, one-day-ahead and three-hour-ahead global solar radiation, respectively, based on weather information using descriptive statistics, factor analysis and binary trees. The main focus is on estimation of the anticipated variations in the forecasts, finding relevant input variables or signal components, and dealing with the nonlinearity of the relationship between the global solar radiation and the input variables. The methods have been tested on the meteorological and global solar radiation data obtained at an observation site, and the results show that they are promising to be used to keep the balance between demand and supply of electric power in the near future power systems with a large number of photovoltaic systems installed. We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system. Abstract With the increasing use of large-scale grid-connected photovoltaic system, accurate forecast approach for the power output of photovoltaic system has become an important issue. In order to forecast the power output of a photovoltaic system at 24-hour-ahead without any complex modeling and complicated calculation, an artificial neural network based approach is proposed in this paper. The improved back-propagation learning algorithm is adopted to overcome shortcomings of the standard back-propagation learning algorithm. Similar day selection algorithm based on forecast day information is proposed to improve forecast accuracy in different weather types. Forecasting results of a photovoltaic system show that the proposed approach has a great accuracy and efficiency for forecasting the power output of photovoltaic system. An accurate forecast of the exploitable energy from Renewable Energy Sources, provided 24 h in advance, is becoming more and more important in the context of the smart grids, both for their stability issues and the reliability of the bidding markets. This work presents a comparison of the PV output power day-ahead forecasts performed by deterministic and stochastic models aiming to find out the best performance conditions. In particular, we have compared the results of two deterministic models, based on three and five parameters electric equivalent circuit, and a hybrid method based on artificial neural network. The forecasts are evaluated against real data measured for one year in an existing PV plant located at SolarTechlab in Milan, Italy. In general, there is no significant difference between the two deterministic models, being the three-parameter approach slightly more accurate (NMAE three-parameter 8.5 vs. NMAE five-parameter 9.0 ). The artificial neural network, combined with clear sky solar radiation, generally achieves the best forecasting results (NMAE 5.6 ) and only few days of training are necessary to provide accurate forecasts.