2008
DOI: 10.1016/j.renene.2007.10.004
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Neuro-fuzzy dynamic model with Kalman filter to forecast irradiance and temperature for solar energy systems

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Cited by 78 publications
(26 citation statements)
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“…As a consequence, one can remark that Tassadducq et al [17] used in 2002 a backpropagation neural network to forecast the temperature at a given hour of the next day, only using the temperature at the same hour of the present day. In 2008, Chaabene and Ben Ammar [18] introduced a new methodology for dynamic forecasting of meteorological parameters. An ANFIS [19][20][21] neuro-fuzzy system offered daily time distribution of irradiance and ambient temperature relying on the meteorological behavior during the days before.…”
Section: Figure 1 Diagram Of the Optienr Project In Red The Forecamentioning
confidence: 99%
“…As a consequence, one can remark that Tassadducq et al [17] used in 2002 a backpropagation neural network to forecast the temperature at a given hour of the next day, only using the temperature at the same hour of the present day. In 2008, Chaabene and Ben Ammar [18] introduced a new methodology for dynamic forecasting of meteorological parameters. An ANFIS [19][20][21] neuro-fuzzy system offered daily time distribution of irradiance and ambient temperature relying on the meteorological behavior during the days before.…”
Section: Figure 1 Diagram Of the Optienr Project In Red The Forecamentioning
confidence: 99%
“…The views of theresults presented here,theirinvestigationlooks promising. Finally, forhorizonsh+24 and m+5, there are stilltoo few studiesusing theMLP.HoweverasMellitandPavan [27] andChaabeneandBen Ammar [57]we believeandhave shown that theMLPwereadapted to thesesituations. In addition,our approachwith the use oftime index appears to be efficient.…”
Section: Resultsmentioning
confidence: 99%
“…However, as in most of the similar works, the energy model assumes that short term energy replenishment schedule for nodes is known. While there are studies on prediction of solar irradiation such as [20], [21] and [22]; few have used Kalman filtering techniques, and none of them combines online proportional time fair resource allocation algorithm with such a predictor.…”
Section: Related Workmentioning
confidence: 99%