Energy Analytics and Comparative Performance Analysis Of Machine Learning Classifiers On Power Boiler Dataset

Dhanalakshmi, J.; Ayyanathan, N.; Pandian, N.S.

doi:10.1109/icoac48765.2019.246819

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The characteristics of the job costing method are that it can not only allocate and calculate costs, but also analyze the flow of resources according to the cause-effect relationship. In this paper, we propose a standard cost prediction model for power grid production and operation under the multidimensional lean management model, analyze the power supply cost of power grid from the perspective of big data, and dig deeper and analyze the production and operation cost data of power grid [3].…”

Section: Introductionmentioning

confidence: 99%

Standard Cost Forecasting Model for Power Grid Production and Operation Under Multidimensional Lean Management Model

Wang¹,

Wang²,

Xu³

et al. 2023

Atlantis Highlights in Intelligent Systems

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The current conventional standard cost forecasting model for power grid production and operation is mainly based on the analysis of historical data of power grid operation by combining with big data technology, which leads to poor forecasting effect due to the lack of analysis of cost drivers. In this regard, we propose a standard cost forecasting model for power grid production and operation under the multidimensional lean management model. The interval number is used as the scale of the operating cost of the grid enterprise, and the interval number is processed to remove uncertainty, and the drivers of the grid operating cost are analyzed and merged, and the cost prediction model is constructed by combining with BP neural algorithm. In the experiments, the proposed model is verified for prediction accuracy. The analysis of the experimental results shows that when the proposed model is used to predict the grid operation cost, the root mean square error value of the model is low and has a high prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

Standard Cost Forecasting Model for Power Grid Production and Operation Under Multidimensional Lean Management Model

Wang¹,

Wang²,

Xu³

et al. 2023

Atlantis Highlights in Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…The algorithms are implemented and root mean square has been calculated for measuring the demand needs in power generation. The application is used for determining the requirement of power distribution in various energy distributions [24], [25]. The ANN model is used for predicting the power consumption for quick estimating the energy needs in both demand and requirement of energy variables.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of demand forecasting in energy consumption based on ensemble model

Dhanalakshmi

Ayyanathan

2022

Bulletin EEI

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Over the previous decade, energy usage has increased exponentially all over the world. The machine learning algorithms are used to classify the demand and requirement of off and evening peak load of southern regional load dispatch centre (SRLDC) data. In this paper, data are classified based on demand and requirement of both evening and off peak of day wise southern regional grid of Andhra Pradesh, Karnataka, Kerala, Tamilnadu, and Pondicherry of different states are proposed. The machine learning algorithms like k-nearest neighbors (KNN), random forest, and logistic regression have been adopted to classify the model. To improve this model efficiency, an ensemble learning method is used to increase the accuracy. The performance measure of state-wise outcome is determined by classifying its demand and requirement needs over its state energy consumption and with different classification algorithms and it is improved by using a combined method of ensemble model with accuracy of 86%.

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A systematic review of big data in energy analytics using energy computing techniques

Dhanalakshmi

Ayyanathan

2021

Concurrency and Computation

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In big data and machine learning, energy analytics has shown rapid development in the past decade. With this development in energy analytics, the energy data has exponentially increased well in different areas and domains. Energy computing techniques have been used in various domains, particularly in energy data to handle the data analysis process. This paper focuses on explaining the concept and evolution of various research questions formulation, data extraction, quality valuation, search strategy, study selection and reporting the results in energy-oriented domains to evaluate energy-computing techniques. The thermal power plant, power system and smart grid datasets were highly used out of plenty of other datasets discussed and the ANN algorithm is most commonly used to classify the dataset. Different sets of algorithms are used for data analysis, classification, converting structured data to unstructured data, regression, visualization, and forecasting. Journal publications from the year 2012 to 2020 have reviewed and adopted a systematic approach to identify the findings of all relevant research methodologies in the defined research domain.

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Energy Analytics and Comparative Performance Analysis Of Machine Learning Classifiers On Power Boiler Dataset

Cited by 6 publications

References 13 publications

Standard Cost Forecasting Model for Power Grid Production and Operation Under Multidimensional Lean Management Model

Standard Cost Forecasting Model for Power Grid Production and Operation Under Multidimensional Lean Management Model

Performance analysis of demand forecasting in energy consumption based on ensemble model

A systematic review of big data in energy analytics using energy computing techniques

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