Artificial intelligence techniques for photovoltaic applications: A review

Mellit, A.; Kalogirou, Soteris A.

doi:10.1016/j.pecs.2008.01.001

Cited by 690 publications

(261 citation statements)

References 245 publications

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“…The robustness of the DIP, and its comparison with the other commonly used prediction intervals methods, are illustrated in Section 6. In particular, since the available literature on point forecast computation contains a considerable amount of works based on heuristic technique (Mellit and Pavan, 2010;Mellit and Kalogirou, 2008;Sfetsos and Coonick, 2000;Behrang et al, 2010), Section 6 also assesses the performances of the proposed DPI coupled with an ANFIS (adaptive neuro-fuzzy inference system) point forecast model. The main findings of the work and its applicability are summarized in Section 7.…”

Section: Introductionmentioning

confidence: 99%

Model-free computation of ultra-short-term prediction intervals of solar irradiance

2016

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We propose an ultra-short-term dynamic interval predictor (DIP) of solar irradiance. Our DIP relies on experimentally observed correlations between the derivative of the solar irradiance and the forecast error in the next time-step. The main originalities of this DIP are (i) its independence from the method used for the point forecast of solar irradiance, (ii) its independence from the error distribution of the point-forecast method. We compare the DIP with the most common prediction interval methods. By using significant data set covering several months of experimental observations, we have observed higher accuracy and lower width of the prediction intervals of the proposed DIP.

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Section: Introductionmentioning

confidence: 99%

Model-free computation of ultra-short-term prediction intervals of solar irradiance

2016

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“…However, fuel sources are decreasing, and global warming phenomena cause the necessity of urgent search for alternative energy sources. The use of renewable energy (RE) reduces the dependency on fossil fuels, and it is proven that RE has great potential and can be utilized to fulfill world energy demand [1].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing of Decentralized Photovoltaic Generation and Energy Storage Units for Malaysia Residential Household Using Iterative Method

Rahman

Nordin

2016

MATEC Web Conf.

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Abstract. World's fuel sources are decreasing, and global warming phenomena cause the necessity of urgent search for alternative energy sources. Photovoltaic generating system has a high potential, since it is clean, environmental friendly and secure energy sources. This paper presents an optimal sizing of decentralized photovoltaic system and electrical energy storage for a residential household using iterative method. The cost of energy, payback period, degree of autonomy and degree of own-consumption are defined as optimization parameters. A case study is conducted by employing Kuala Lumpur meteorological data, typical load profile from rural area in Malaysia, decentralized photovoltaic generation unit and electrical storage and it is analyzed in hourly basis. An iterative method is used with photovoltaic array variable from 0.1kW to 4.0kW and storage system variable from 50Ah to 400Ah was performed to determine the optimal design for the proposed system.

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“…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%

“…Mellit, (2008) presented a review of artificial intelligence techniques for solar radiation forecasting and concluded that ANN models can be generalized to be used in different locations around the world.…”

Section: Introductionmentioning

confidence: 99%

Hourly global solar irradiation forecasting for New Zealand

2015

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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.

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Artificial intelligence techniques for photovoltaic applications: A review

Cited by 690 publications

References 245 publications

Model-free computation of ultra-short-term prediction intervals of solar irradiance

Model-free computation of ultra-short-term prediction intervals of solar irradiance

Optimal Sizing of Decentralized Photovoltaic Generation and Energy Storage Units for Malaysia Residential Household Using Iterative Method

Hourly global solar irradiation forecasting for New Zealand

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