A model tree approach to forecasting solar irradiance variability

McCandless, Tyler; Haupt, Sue Ellen; Young, George S.

doi:10.1016/j.solener.2015.07.020

Cited by 48 publications

(22 citation statements)

References 30 publications

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“…Haupt, and G.S. Young research, they wanted to solve the problem of solar energy generation especially from the problem caused by weather conditions using Artificial Intelligence [14]. There are problems because there is no direct interaction between teachers and students.…”

Section: Methodsmentioning

confidence: 99%

“…YÜCEL implemented a decision tree by adjusting the technology behavior scale from 50 items previously prepared as a 5 point single-dimensional Likert type, according to the tree classification method [13]. Then McCandless implemented a decision tree by combining additional model options with predictions to reduce errors that exist in the decision tree model [14]. Topîrceanua implemented a decision tree that is the classification tree which will be used for evaluating the students and identifying the students' profiles, strengths, and weaknesses [15].…”

Section: Methodsmentioning

confidence: 99%

“…Some previous research about a decision tree conducted by Yücel et al [13], produced a performance evaluation tool that could be used to classify the shortcomings of teachers and students so that it could be used as a guide to correct the deficiencies. In 2015, McCandless et al [14] found that using a model tree technique for solar power generation can reduce the mean absolute error of the variability prediction between 10% and 55% compared to using climatological average values of the temporal and spatial GHI standard deviation. That means the skill tree model can map out the results of the predictions for proper decision-making.…”

Section: B Related Workmentioning

confidence: 99%

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Study of Skill Tree from Game for Educational World: Application of Skill Tree in Instructional Media of Scientific Writing for Teachers

Ghozali¹,

Asnawi²,

Pramudika³

2019

Proceedings of the 3rd International Conference on Current Issues in Education (ICCIE 2018)

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Indonesia has rules that require students to study for nine years. These rules are implemented for making a generation of a smart nation that can survive in the modern era. Therefore, the assigned teachers must be professional. Professional teachers are also mentioned in the Law that they should write scientific papers. However, many teachers do not know how to write scientific papers. In writing scientific papers, there are several important points that must be considered. The points can be mapped to the existing part of the skill tree. Then the skill tree that is created can be implemented in a form of digital media or a media for helping teachers to learn about writing scientific papers. The skill tree uses a method of learning scientific work that is supposed to improve teachers' knowledge about scientific work. Therefore, the current paper was created to analyze the effect of skill tree use in the field of education, especially its impact on teachers.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Study of Skill Tree from Game for Educational World: Application of Skill Tree in Instructional Media of Scientific Writing for Teachers

Ghozali¹,

Asnawi²,

Pramudika³

2019

Proceedings of the 3rd International Conference on Current Issues in Education (ICCIE 2018)

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“…Recent work has added satellite data to this type of forecasting (McCandless et al 2016b). Some methods also predict the variability of the resource (McCandless et al 2015).…”

Section: Nowcastingmentioning

confidence: 99%

Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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Short-range forecasts for periods on the order of hours to days and up to two weeks ahead are necessary to smoothly run transmission and distribution systems, plan maintenance, protect infrastructure and allocate units. In particular, forecasting the renewable energy resources on a day-to-day basis enables integration of increasing capacities of these variable resources. This chapter describes the basics of this short-range forecasting, beginning with the observation-based "nowcasting" of the first 15 minutes and ranging up to two weeks using numerical weather prediction. We discuss how blending multiple forecasts can increase accuracy and how probabilistic forecasts are being used to quantify the forecast uncertainty.

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“…For utility planners and operators, it is essential to examine the power output variability [9] to aggregate the fleet of PV systems, which is defined as the number of individual PV systems spread out over a geographical area [10]. Several studies presented station-pair correlation analyses by introducing virtual networks.…”

Section: Introductionmentioning

confidence: 99%

Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

Park

Kim

et al. 2020

Energies

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This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather data at every station is not feasible for a fleet owing to the limitation of installation costs. In this study, to monitor a PV fleet efficiently, neighboring PV generation profiles were utilized for fault detection and imputation, as well as solar irradiance. For fault detection from unlabeled raw PV data, K-means clustering was employed to detect abnormal patterns based on customized input features, which were extracted from the fleet PVs and weather data. When a profile was determined to have an abnormal pattern, imputation for the corresponding data was implemented using the subset of neighboring PV data clustered as normal. For evaluation, the effectiveness of neighboring PV information was investigated using the actual rooftop PV power generation data measured at several locations in the Gwangju Institute of Science and Technology (GIST) campus. The results indicate that neighboring PV profiles improve the fault detection capability and the imputation accuracy. For fault detection, clustering-based schemes provided error rates of 0.0126 and 0.0223, respectively, with and without neighboring PV data, whereas the conventional prediction-based approach showed an error rate of 0.0753. For imputation, estimation accuracy was significantly improved by leveraging the labels of fault detection in the proposed scheme, as much as 18.32% reduction in normalized root mean square error (NRMSE) compared with the conventional scheme without fault consideration.

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A model tree approach to forecasting solar irradiance variability

Cited by 48 publications

References 30 publications

Study of Skill Tree from Game for Educational World: Application of Skill Tree in Instructional Media of Scientific Writing for Teachers

Study of Skill Tree from Game for Educational World: Application of Skill Tree in Instructional Media of Scientific Writing for Teachers

Short-Range Forecasting for Energy

Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

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