Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis

Cao, Jinxin; Cao, Shukun

doi:10.1016/j.energy.2006.04.001

Cited by 161 publications

(48 citation statements)

References 8 publications

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“…As the most important MIF of PV power generation, solar irradiance forecasting is the basis of step-wise forecast. Solar irradiance forecast methods include time series [6,13], wavelet analysis and fuzzy logic [14,15], satellite data and sky images [16,17,20] and statistical learning methods such as artificial neural network (ANN) [18,19]. Among the current research, statistical learning methods based forecast models perform better than the other forecast techniques [20].…”

Section: Introductionmentioning

confidence: 97%

Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting

Wang

Zhen

et al. 2015

Energy and Buildings

203

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Section: Introductionmentioning

confidence: 97%

Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting

Wang

Zhen

et al. 2015

Energy and Buildings

203

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“…Cao and Cao [104][105][106][107][108] they all combined wavelet with ANN. Other authors like [109][110][111][112][113][114][115][116][117][118][119][120][121][122][123] used other soft computing techniques like GA, fuzzy logic, Quantum based GA, adaptive neurofuzzy, etc.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Solar Power Forecasting: A Review

K.¹,

Isha²

2016

IJCA

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The increasing demand for energy is one of the biggest reasons behind the integration of solar energy into the electric grids or networks. To ensure the efficient use of energy PV systems it becomes important to forecast information reliably. The accurate prediction of solar irradiance variation can enhance the quality of service. This integration of solar energy and accurate prediction can help in better planning and distribution of energy.Here in this paper, a deep review of methods which are used for solar irradiance forecasting is presented. These methods help in selecting the appropriate forecast technique according to the needs or requirements. This paper also presents the metrics that are used for evaluating the performance of a forecast model.

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“…Recently, the wavelet transform (WT) has been used for this task and for eliminating, or reducing to the least, of this noise. Yousefi et al [3] showed in a recent study that pretreatment of wind speed data series using wavelet transform and could improve the efficiency of a neural network in predicating the wind speed in short-term horizons) [4][5][6]. For building a prediction model, the redundant highly auto-correlated wind speed data could be eliminated to improve the prediction accuracy as it has been previously shown in the literature that wind speed data has a high degree of autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Yousefi

Hooshyar

Remezani

et al. 2015

Int. J. Adv. Intell. Informatics

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Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurate

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Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis

Cited by 161 publications

References 8 publications

Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting

Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting

Solar Power Forecasting: A Review

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

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