Forecasting power output of photovoltaic system based on weather classification and support vector machine
10 Nov 2011-pp 1-6
TL;DR: A one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM and results show the proposed forecast model for grid-connected PV systems is effective and promising.
Abstract: Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for the system reliability and promoting large scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machine. In the process, the weather conditions are firstly divided into four types which are clear sky, cloudy day, foggy and rainy day. One-day-ahead PV power output forecasting model for single station is derived based on the weather forecasting data and historically actual power output data as well as the principle of Support Vector Machine (SVM). After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected photovoltaic systems is effective and promising.
Citations
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TL;DR: This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends, and represents the most up-to-date compilation of solarPower forecasting studies.
829 citations
TL;DR: In this paper, a comprehensive and systematic review of the direct forecasting of PV power generation is presented, where the importance of the correlation of the input-output data and the preprocessing of model input data are discussed.
Abstract: To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.
626 citations
Cites background or methods from "Forecasting power output of photovo..."
...In PV power forecasting, the most commonly used post-processing techniques are anti-normalization [32] and wavelet reconstruction [37]....
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...[32] proposed an algorithm to forecast PV power output based upon weather classification and SVM....
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...Stationary, historical lag identification [28], trend-free time series [31], normalization [32], wavelet transform (WT) [33], and self-organizing map (SOM) [34] are useful methods to pre-process the input data....
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...The performance of SVR highly depends on the selection of kernel function and its parameters [32]....
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TL;DR: A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented and achieves better prediction accuracy than the simple SVR and traditional ANN methods.
Abstract: To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.
390 citations
Cites methods from "Forecasting power output of photovo..."
...The techniques used consist of support vector regression (SVR) [7], [8], ANN [9], [10], and hybridANN methods [11]....
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...For comparison purpose, the methods of simple SVR [8] and ANN [21], [22] are also tested by using the same databases after classification based on the SOM and LVQ approach as done by the proposed method....
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TL;DR: In this paper, the authors present a detailed technical overview of microgrid and smart grid in light of present development and future trend, including existing technical challenges, communication features, policies and regulation, etc.
Abstract: The modern electric power systems are going through a revolutionary change because of increasing demand of electric power worldwide, developing political pressure and public awareness of reducing carbon emission, incorporating large scale renewable power penetration, and blending information and communication technologies with power system operation. These issues initiated in establishing microgrid concept which has gone through major development and changes in last decade, and recently got a boost in its growth after being blessed by smart grid technologies. The objective of this paper is to presents a detailed technical overview of microgrid and smart grid in light of present development and future trend. First, it discusses microgrid architecture and functions. Then, smart features are added to the microgrid to demonstrate the recent architecture of smart grid. Finally, existing technical challenges, communication features, policies and regulation, etc. are discussed from where the future smart grid architecture can be visualized.
343 citations
Cites methods from "Forecasting power output of photovo..."
...For instance, in the literature, the methods of SVM [63,64], vector auto regression theory [64], Bayesian Method with Monte Carlo [65] are used for PV....
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...Some of the artificial intelligence based forecasting models are Artificial Neural Network(ANN) [51], Grey-Back Propagation (GBP) Neural Network [52], improved variable learning rate back propagation (IVL-BP) [53], support vector machines (SVMs) [54], Least Squares-Support Vector Machine (LS-SVM) Algorithm [55], particle swarm optimization (PSO) [56], and fuzzy logic (FL) [57]....
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TL;DR: Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs, and the estimated results of the proposed PV power forecasting model coincide well with measurement data as discussed by the authors.
Abstract: Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.
289 citations
Cites methods from "Forecasting power output of photovo..."
...Support vector machine (SVM) was also applied to predict PV power outputs [12]....
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References
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Proceedings Article•
03 Dec 1996TL;DR: This work compares support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space and expects that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.
Abstract: A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.
4,009 citations
TL;DR: In this paper, a multilayer perceptron (MLP) model was proposed to forecast the solar irradiance on a base of 24h using the present values of the mean daily solar irradiances and air temperature.
749 citations
24 Jul 2000
TL;DR: The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for financial forecasting applications.
Abstract: The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for financial forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results for stock price prediction are also presented.
303 citations
TL;DR: In this paper, the authors use satellite images as a data source for short-term forecasting of solar irradiance, which is an important issue for many fields of solar energy applications.
254 citations
TL;DR: In this paper, the authors discuss the distribution zone and current developmental situation of solar energy in China, and some application practice is described, such as solar energy greenhouse, solar energy hearth, solar water heater, solar lighting system, solar power pump, distributed generation (DG), grid-connect photovoltaic generation (GPG) and wind-solar hybrid system.
Abstract: The steady and maintainable electric power provides the development momentum of a country's industrialization, which is indispensable to every country at present. It is well known that China is the largest developing country in the world. With the rapid development of economy and society, energy demand of Chinese society is increasing in an incredible speed, i.e., the annual accumulative total capacity of electric energy is about 0.1 billion kW, and the most of them is provided by the fossil fuel resource, and the share is about 90% in China. Certainly, it is a very inappropriate energy structure, so the sustainable development of country is impossible in future, the status must be improved in order to achieve sustainable development. Fortunately, China has large country area, and there are abundant solar resources. Development and application of solar energy have been regarded by the government and ordinary people, and they thought that solar energy can provide more and more electric energy in future, and more and more actual examples have been applied in the last decades, which are supported by central government and local governments. This paper discusses the distribution zone and current developmental situation of solar energy in China. Then, some application practice is described, such as solar energy greenhouse, solar energy hearth, solar water heater, solar lighting system, solar water pump, distributed generation (DG), grid-connect photovoltaic generation (GPG) and wind–solar hybrid system. The policies and law of China central government and local governments are described in the following paragraph. At the end, the developmental prospect of photovoltaic (PV) in future China and the development barriers and recommendations are introduced.
200 citations