Signal quality based power output prediction of a real distribution transformer station using M5P model tree

Akgündoğdu, Abdurrahim; Oz, Ismail; Uzunoğlu, Cengiz Polat

doi:10.1016/j.epsr.2019.106003

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…M5P is more sensitive to data segmentation and gives better results with longer data set as input. The following steps are involved in implementing the M5P technique to produce forecasts for a given problem as detailed in [47], [48]:…”

Section: M5p Techniquementioning

confidence: 99%

Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India

Srivastava

Tripathi²,

Kumar

et al. 2022

IEEE Access

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The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly as possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing preventive measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, and M5P techniques have been discussed and implemented for forecasting novel coronavirus disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy of the aforementioned ML approaches, a preliminary sample-study has been conducted on the first wave of the COVID-19 pandemic scenario for three different countries including the United States of America (USA), Italy, and Australia. Furthermore, the contributions of this study are extended by conducting an in-depth forecast study on COVID-19 pandemic scenarios for the first, second, and third waves in India. An accurate forecasting model has been proposed, which has been constructed on the basis of the results of the aforementioned forecasting models of COVID-19 pandemic scenarios. The findings of the research highlight that LR is a potential approach that outperforms all other forecasting models tested herein in the present COVID-19 pandemic scenario. Finally, the LR approach has been used to forecast the likely onset of the fourth wave of COVID-19 in India.INDEX TERMS Death forecasting, linear regression (LR), M5P, machine learning (ML), novel coronavirus (nCOV), COVID-19 forecasting, SMO regression.

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Section: M5p Techniquementioning

confidence: 99%

Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India

Srivastava

Tripathi²,

Kumar

et al. 2022

IEEE Access

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“…Predictions also appear on tree leaves [37]. M5P tree model has the ability to predict numerically continuous variables from numerical traits and the predicted results appear as multivariate linear regression models on tree leaves [38]. The criterion of division in a node is based on the selection of the standard deviation of the output values that reach that node as a measure of error.…”

Section: M5pmentioning

confidence: 99%

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Hoan¹,

Dũng²,

Thu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

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“…It also has the advantage of being able to handle continuous variables and missing data. The leaves of the tree are multiple linear regression models [84]. It has been used in various studies such as estimating hybrid energy consump-tion [85], maximum power point trackers [86], prediction of wind power [87,88], modelling and predicting traffic accidents duration on urban freeway [89], predicting concrete strength [90], prediction of mechanical properties of leaf [91], predict precipitation index [92].…”

Section: Correlation Of Factors Affecting Resource Utilization Of Hadoop Clustersmentioning

confidence: 99%

Resource and Performance Modelling of Hadoop Clusters Using Machine Learning

Tariq¹

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<p>There is a huge and rapidly increasing amount of data being generated by social media, mobile applications and sensing devices. Big data is the term usually used to describe such data and is described in terms of the 3Vs - volume, variety and velocity. In order to process and mine such a massive amount of data, several approaches and platforms have been developed such as Hadoop. Hadoop is a popular open source distributed and parallel computing framework. It has a large number of configurable parameters which can be set before the execution of jobs to optimize the resource utilization and execution time of the clusters. These parameters have a significant impact on system resources and execution time. Optimizing the performance of a Hadoop cluster by tuning such a large number of parameters is a tedious task. Most current big data modeling approaches do not include the complex interaction between configuration parameters and the cluster environment changes such as use of different datasets or types of query. This makes it difficult to predict for example the execution time of a job or resource utilization of a cluster. Other attributes include configuration parameters, the structure of query, the dataset, number of nodes and the infrastructure used. Our first main objective was to design reliable experiments to understand the relationship between attributes. Before designing and implementing the actual experiment we applied Hazard and Operability (HAZOP) analysis to identify operational hazards. These hazards can affect normal working of cluster and execution of Hadoop jobs. This brainstorming activity improved the design and implementation of our experiments by improving the internal validity of the experiments. It also helped us to identify the considerations that must be taken into account for reliable results. After implementing our design, we characterized the relationship between different Hadoop configuration parameters, network and system performance measures. Our second main objective was to investigate the use of machine learning to model and predict the resource utilization and execution time of Hadoop jobs. Resource utilization and execution time of Hadoop jobs are affected by different attributes such as configuration parameters and structure of query. In order to estimate or predict either qualitatively or quantitatively the level of resource utilization and execution time, it is important to understand the impact of different combinations of these Hadoop job attributes. You could conduct experiments with many different combinations of parameters to uncover this but it is very difficult to run such a large number of jobs with different combinations of Hadoop job attributes and then interpret the data manually. It is very difficult to extract patterns from the data and give a model that can generalize for an unseen scenario. In order to automate the process of data extraction and modeling the complex behavior of different attributes of Hadoop job machine learning was used. Our decision tree based approach enabled us to systematically discover significant patterns in data. Our results showed that the decision tree models constructed for different resources and execution time were informative and robust. They were able to generalize over a wide range of minor and major environmental changes such as change in dataset, cluster size and infrastructure such as Amazon EC2. Moreover, the use of different correlation and regression techniques, such as M5P, Pearson's correlation and k-means clustering, confirmed our findings and provided further insight into the relationship of different attributes and with each other. M5P is a classification and regression technique that predicted the functional relationships among different job attributes. The use of k-means clustering allowed us to see the experimental runs that shows similar resource utilization and execution time. Statistical significance tests, were used to validate the significance of changes in results of different experimental runs, also showed the effectiveness of our resource and performance modelling and prediction method.</p>

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Signal quality based power output prediction of a real distribution transformer station using M5P model tree

Cited by 8 publications

References 17 publications

Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India

Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Resource and Performance Modelling of Hadoop Clusters Using Machine Learning

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