Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production

Ferlito, Sergio; Adinolfi, Giovanna; Graditi, Giorgio

doi:10.1016/j.apenergy.2017.07.124

Cited by 74 publications

(37 citation statements)

References 28 publications

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“…The maximal value of committees is limited to 100 in Cubist modeling [41]. The other parameter neighbors, is an integer with 0-9 range [42]. We found that the RMSE cv initially significantly reduced as the committees increased and then stabilized at a higher committees, and a model with neighbors of 0 performed better than the model with neighbors of 1.…”

Section: Model Developmentmentioning

confidence: 82%

“…Two tuning parameters require users to make choices: "committees" and "neighbors". The committees is the number of iterative model trees to grow in sequence whose maximal value is limited to 100 [41].The neighbors is the number of neighboring training points which can be used to adjust the final predictions and is an integer with range 0-9 [42]. Considering the trade-off between computing costs and model predictive performance, we tried 20 possible values for committees (1, 6, 11, . .…”

Section: (3) Cubist Modelmentioning

confidence: 99%

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Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Lei

Shen

2019

Forests

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Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch–spruce–fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.

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Section: Model Developmentmentioning

confidence: 82%

Section: (3) Cubist Modelmentioning

confidence: 99%

Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Lei

Shen

2019

Forests

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“…SMPPT converters work in continuous changing conditions as it is evident exploiting historical climatic data series (temperature, irradiance, wind speed) monitored by weather stations [3] and appropriately acquired (Figure 2). Since it is difficult to identify the worst operating condition considering both the ambient temperature and the irradiance, the annual frequency of different meteoclimatic conditions can be analyzed in order to identify the most frequent ones.…”

Section: System Reliability 204mentioning

confidence: 99%

Reliability Prediction of Smart Maximum Power Point Converter for PV Applications

Adinolfi¹,

Graditi²

2017

System Reliability

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The photovoltaic generation distribution supports new energetic scenario development (Net Zero Energy Cluster and DC microgrids). In this context, smart maximum power point (SMPPT) converter represents innovative systems that are able to monitor operating conditions, communicate energetic production data and signal a fault condition. The Smart Maximum Power Point role becomes crucial, and critical aspects such as efficiency and reliability have to be taken into account from the beginning of the design. In this chapter, the idea is to review different reliability prediction models for electronic components focusing on the military ones with the analysis of a case study related to a Smart Maximum Power Point converter in photovoltaic applications.

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“…Accurate STLF predictions play a vital role in electrical department load dispatch, unit commitment, and electricity market trading [1]. With the permeation of renewable resources in grids and the technological innovation of electric vehicles, load components become more complex and make STLF difficult; therefore, strict requirements of stability and accuracy are needed [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

Lin

et al. 2018

Energies

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Abstract:To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting.

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Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production

Cited by 74 publications

References 28 publications

Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Reliability Prediction of Smart Maximum Power Point Converter for PV Applications

Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

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