Identification and Handling of Critical Irradiance Forecast Errors Using a Random Forest Scheme – A Case Study for Southern Brazil

Kratzenberg, Manfred Georg; Zurn, H.H.; Revheim, Pål Preede; Beyer, Hans Georg

doi:10.1016/j.egypro.2015.07.900

Cited by 11 publications

(3 citation statements)

References 2 publications

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“…As the numbers of trees are limitless, so the classifier never over fits the model. Another advantage of this technique is that this model can handle missing values and the classifier can be tuned for categorical parameters 45–47 . The pseudo‐code of the classification model and prediction process is shown below.…”

Section: Proposed Methodologymentioning

confidence: 99%

An experimental approach to evaluate machine learning models for the estimation of load distribution on suspension bridge using FBG sensors and IoT

Mohapatra¹,

Khanna

Gupta

et al. 2020

Computational Intelligence

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Most of the tragedies on any bridge structure have been the cause of high‐density crowd behavior as a response to trampling as well as the crushing scenario. Therefore, it is most important to monitor such unforeseen situations by sensing the load imposed on the bridge structures. This scenario may arise where crowd movement is huge on these types of bridges. Similarly, the fiber Bragg grating (FBG) is a promising technology for structural health monitoring applications. In this work, an Internet of Things based FBG optical sensing scheme is proposed to monitor real‐time strain distribution throughout the bridge structures and localization of load imposed on the structure from a central control room. A suspension bridge model is designed by referring to a real bridge scenario and these FBG sensors are deployed to validate the proposed machine learning models. In this article, the performances of two machine learning strategies are discussed for the accurate estimation of load and its position by acquiring high sensitive FBG sensors signals at a very high data rate. The algorithms include K‐nearest neighbor (KNN) and random forest (RF); which are applied on each sensing data source, and then validated using a prototype suspension bridge model integrated with three FBG sensors (1532 nm, 1538 nm, and1541 nm) on a single optical fiber cable.

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Section: Proposed Methodologymentioning

confidence: 99%

An experimental approach to evaluate machine learning models for the estimation of load distribution on suspension bridge using FBG sensors and IoT

Mohapatra¹,

Khanna

Gupta

et al. 2020

Computational Intelligence

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“…… (1) The random forest algorithm is also suited for wide range of datasets [26,27]. It is a collection of many classification and regression trees.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Soil N-P-K Prediction Using Location and Crop Specific Random Forest Classification Technique in Precision Agriculture

Mohapatra

Keswani

Lenka

2017

ijarcs

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Agriculture is a basic and most important profession of any country as it balances the food requirement and also the essential raw materials of industry. In the similar sense, the adaptation of implementing smart technology in agriculture practices needs to be focused on better land productivity. The fertilizer and manure should be precisely applied during agriculture practice throughout the countryside with the use of digital technologies. This motivates us to develop a reactive web application which will accurately predict the required soil N-P-K (Nitrogen-Phosphorus-Potassium) content by utilizing one time soil testing results of available soil N-P-K contents as per the yield target. This predictive model is designed by considering standard experimental data sets from Indian Council for Agriculture Research (ICAR), India. The prediction model is designed using Random Forest (RF) algorithm which is capable of handling large dataset. The predicted N-P-K content are shown in a reactive R Shiny user interface to notify required N-P-K values for the necessary action by the farmer. The complete web based prediction model is efficiently conveying the required N-P-K content information of the particular farm location to the farmer as well as the agriculture specialists.

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“…The results of this research work show that the performance of the RFs model is better than the results obtained by linear, exponential, and logarithmic models. In the meanwhile, in Kratzenberg et al (2015), the authors used the RFs model for predicting hourly and the daily solar radiation. Here, the internal RFs parameters were not mentioned.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the output current of a photovoltaic grid-connected system using random forests technique

Ibrahim

Khatib

Mohamed³

et al. 2017

Energy Exploration & Exploitation

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This study presents a prediction technique for the output current of a photovoltaic grid-connected system by using random forests technique. Experimental data of a photovoltaic grid-connected system are used to train and validate the proposed model. Three statistical error values, namely root mean square error, mean bias error, and mean absolute percentage error, are used to evaluate the developed model. Moreover, the results of the proposed technique are compared with results obtained from an artificial neural network-based model to show the superiority of the proposed method. Results show that the proposed model accurately predicts the output current of the system. The root mean square error, mean absolute percentage error, and mean bias error values of the proposed method are 2.7482, 8.7151, and À2.5772%, respectively. Moreover, the proposed model is faster than the artificial neural network-based model by 0.0801 s.

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Identification and Handling of Critical Irradiance Forecast Errors Using a Random Forest Scheme – A Case Study for Southern Brazil

Cited by 11 publications

References 2 publications

An experimental approach to evaluate machine learning models for the estimation of load distribution on suspension bridge using FBG sensors and IoT

An experimental approach to evaluate machine learning models for the estimation of load distribution on suspension bridge using FBG sensors and IoT

Soil N-P-K Prediction Using Location and Crop Specific Random Forest Classification Technique in Precision Agriculture

Modeling of the output current of a photovoltaic grid-connected system using random forests technique

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