Hybrid Predictive Models for Accurate Forecasting in PV Systems

Ogliari, Emanuele; Grimaccia, Francesco; Leva, Sonia; Mussetta, Marco

doi:10.3390/en6041918

Cited by 89 publications

(44 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some works present models specifically dedicated to the hourly power generation forecasting in PV plants [8][9][10][11]. The most applied technique in these forecasting models is a specific soft-computing technique known as Artificial Neural Networks (ANNs) but some papers use simple physical methods [3,12,13]. Furthermore, in order to define the accuracy of the prediction, some error indexes are introduced to evaluate the performances of the forecasting models.…”

Section: Open Accessmentioning

confidence: 99%

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

et al. 2015

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Open Accessmentioning

confidence: 99%

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

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The hourly error e h is the starting definition given as the difference between the hourly mean values of the power measured in the h-th hour P m,h and the forecast P p,h provided by the adopted model [32,35]:…”

Section: Evaluation Indexesmentioning

confidence: 99%

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract:The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network's main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility.

show abstract

“…For this reason and as a common practice, researchers apply different techniques to a certain problem to find the best method suited to solve it. More often authors try to combine two or more methods in a single hybrid one [32][33][34]. The aim is to use different models with unique features to overcome the single negative performance and finally improve the performances.…”

Section: Introductionmentioning

confidence: 99%

Very accurate parameter estimation of single- and double-diode solar cell models using a modified artificial bee colony algorithm

Jamadi

Merrikh-Bayat

Bigdeli

2015

Int J Energy Environ Eng

104

View full text Add to dashboard Cite

This paper proposes an effective method for very accurate parameter estimation of single-and doublediode solar cell models. For this purpose, unknown parameters of model are estimated by minimization of an objective function using a new highly effective modified version of artificial bee colony (ABC) algorithm. The proposed algorithm is used for parameter estimation of single-and double-diode models and the results are compared, from different aspects, with recently developed related works (including those applied GA, CPSO, PS, SA, IGHS, ABSO, ABC, GGHS, and IADE for this purpose). The proposed algorithm is also compared with other modified explanations of ABC algorithm. The results show that the proposed strategy is considerably faster and more accurate compared to all the previous studies.

show abstract

Hybrid Predictive Models for Accurate Forecasting in PV Systems

Cited by 89 publications

References 15 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

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

Very accurate parameter estimation of single- and double-diode solar cell models using a modified artificial bee colony algorithm

Contact Info

Product

Resources

About