Artificial neural networks in renewable energy systems applications: a review

Kalogirou, Soteris A.

doi:10.1016/s1364-0321(01)00006-5

Cited by 987 publications

(420 citation statements)

References 14 publications

Supporting

Mentioning

404

Contrasting

Unclassified

Order By: Relevance

“…In recent years, much research has been conducted on the application of artificial intelligence techniques to load forecasting problems [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. However, the models that have received the most extensive attention are undoubtedly the ANNs, cited among the most powerful computational tools ever developed.…”

Section: Artificial Intelligence Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Regional load forecasting in Taiwan––applications of artificial neural networks

Hsu

Chen

2003

Energy Conversion and Management

206

View full text Add to dashboard Cite

Section: Artificial Intelligence Based Methodsmentioning

confidence: 99%

“…ANN models can handle large and complex systems with many interrelated parameters. They simply seem to ignore excess data that are of minimal significance and concentrate instead on the more important inputs [16].…”

Section: Artificial Intelligence Based Methodsmentioning

confidence: 99%

Regional load forecasting in Taiwan––applications of artificial neural networks

Hsu

Chen

2003

Energy Conversion and Management

206

View full text Add to dashboard Cite

“…Kalogirou, (2001) has reviewed the use of ANN in renewable energy systems applications while Mellit and Kalogirou, (2008) and Mellit et al (2009) reviewed ANN's in photovoltaic applications and for sizing of photovoltaic systems respectively. Similarly authors such as Esen et al (2008) have examined adaptive neuro-fuzzy inference systems (ANFIS) and ANN models of ground-coupled heat pump (GCHP) systems.…”

Section: Introductionmentioning

confidence: 99%

Hourly global solar irradiation forecasting for New Zealand

2015

View full text Add to dashboard Cite

With the growing use of solar thermal energy systems and small scale photovoltaic power generation by domestic users, there is increasing need to develop intelligent controllers that allow these users to efficiently manage the energy generated by these systems. Ideally these intelligent controllers will be able to forecast the availability and magnitude of the solar resource to plan in advance for periods when the solar irradiance magnitude is small or unavailable. In addition, the method used to provide this forecast needs to be adaptable to a range of timescales and locations.With this in mind, this study examined the possibility of providing a 24-hour ahead forecast of hourly global solar irradiation in New Zealand using several approaches but with particular reference to nonlinear autoregressive recurrent neural networks with exogenous inputs (NARX).Hourly time series data for nine historic weather variables recorded over a three year period was Zealand. The results demonstrate the ability of the NARX approach to forecast irradiation values at a later time and across a number of different locations. As such it is foreseeable that such an approach could serve as the basis of a forecasting system in future intelligent controllers.

show abstract

“…Lately it has also received attention as a tool in renewable energy system prediction and modelling (Kalogirou, 2001). A back propagation neural network using the Levenberg-Marquardt (LM) algorithm has been applied to a hybrid upflow anaerobic sludge blanket reactor to predict the bio-degradation and bio-hydrogen production using distillery wastewater (Sridevi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

Pandey

Pan

Das

et al. 2015

Bioresource Technology

View full text Add to dashboard Cite

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalize well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based datadriven modelling strategy can be a good candidate for developing models for other types of fuels as well.

show abstract

Artificial neural networks in renewable energy systems applications: a review

Cited by 987 publications

References 14 publications

Regional load forecasting in Taiwan––applications of artificial neural networks

Regional load forecasting in Taiwan––applications of artificial neural networks

Hourly global solar irradiation forecasting for New Zealand

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

Contact Info

Product

Resources

About