Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques

Mandal, Paras; Madhira, Surya T. S.; Haque, Ashraf Ul; Meng, Julian; Pineda, Ricardo

doi:10.1016/j.procs.2012.09.080

Cited by 182 publications

(97 citation statements)

References 8 publications

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“…In [25] the authors couple two stochastic methods, wavelet time series analysis with the ANN, and perform one-hour-ahead PV output forecasting and finally in [26] the authors use two dynamic NNs to forecast 1 h ahead the power yield of PV plant.…”

Section: Energy Forecast Modelsmentioning

confidence: 99%

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

et al. 2015

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Abstract:The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications.

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Section: Energy Forecast Modelsmentioning

confidence: 99%

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

et al. 2015

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“…In this section, the WNN model that combines the ANN and the wavelets theory is selected to be the predictor because it is familiar to our team and it has the following advantages [18][19][20][21][22]:…”

Section: Prediction Model Of the Wavelet Neural Networkmentioning

confidence: 99%

“…The popularity of wavelets with compact supports is due mainly to their relation to the dyadic multiresolution analysis that dominates wavelet research [20]. Historically, the continuous wavelet transform came first, which is defined as below:…”

Section: Prediction Model Of the Wavelet Neural Networkmentioning

confidence: 99%

“…Its basic learning rule is the steepest descent method, which minimizes the sum of squared errors according to the back-propagation to continuously adjust the weights and thresholds of the network [19]. The popularity of wavelets with compact supports is due mainly to their relation to the dyadic multiresolution analysis that dominates wavelet research [20]. Historically, the continuous wavelet transform came first, which is defined as below:…”

Section: Prediction Model Of the Wavelet Neural Networkmentioning

confidence: 99%

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The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather

Gao

Zhu

et al. 2016

Sustainability

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Abstract:With the growing influence of fog and haze (F-H) weather and the rapid development of distributed energy resources (DERs) and smart grids, the concept of the virtual power plant (VPP) employed in this study would help to solve the dispatch problem caused by multiple DERs connected to the power grid. The effects of F-H weather on photovoltaic output forecast, load forecast and power system dispatch are discussed according to real case data. The wavelet neural network (WNN) model was employed to predict photovoltaic output and load, considering F-H weather, based on the idea of "similar days of F-H". The multi-objective optimal dispatch model of a power system adopted in this paper contains several VPPs and conventional power plants, under F-H weather, and the mixed integer linear programming (MILP) and the Yalmip toolbox of MATLAB were adopted to solve the dispatch model. The analysis of the results from a case study proves the validity and feasibility of the model and the algorithms.

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“…The autoregressive with exogenous model uses numerical weather predictions as input. (Mandal et al, 2012) forecasts one-hour-ahead power output of a PV system using a combination of wavelet transform and neural network techniques by incorporating the interactions of PV system with solar radiation and temperature data. (Pedro and Coimbra, 2012) predicts 1 and 2 h-ahead solar power of a PV system comparing several forecasting techniques without exogenous inputs such as Auto-Regressive Integrated Moving Average, k-Nearest-Neighbors, Artificial Neural Networks, and Neural Networks optimized by Genetic Algorithms.…”

mentioning

confidence: 99%

PV power forecast using a nonparametric PV model

2015

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Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%.

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Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques

Cited by 182 publications

References 8 publications

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather

PV power forecast using a nonparametric PV model

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