RETRACTED ARTICLE: ANN and fuzzy based household energy consumption prediction with high accuracy

Balachander, K.; Paulraj, D.

doi:10.1007/s12652-020-02455-4

Cited by 16 publications

(5 citation statements)

References 37 publications

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“…The author took into account the server's refresh times when processing queries. Balachander, K. [32] introduced task scheduling that takes bandwidth into account as a resource. The development of a nonlinear programming paradigm has made resource allocation between tasks possible.…”

Section: Plos Onementioning

confidence: 99%

An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

Paulraj

Sethukarasi²,

Neelakandan³

et al. 2023

PLoS ONE

Self Cite

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Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species’ obligate brood parasitism (laying eggs in other species’ nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.

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Section: Plos Onementioning

confidence: 99%

An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

Paulraj

Sethukarasi²,

Neelakandan³

et al. 2023

PLoS ONE

Self Cite

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“…Gulay and Duru have combined three different models: ARDL (autoregressive distributed lag model), EMD (empirical mode decomposition), and ANN (artificial neural network) for the predictive analytics of energy systems and prices; the proposed hybrid forecasting algorithms provided better results by improving the forecasting accuracy [16]. The publication [17] has displayed a novel hybrid model ANFIS which consolidates both ANN and fuzzy frameworks for prediction future power utilization; the result has proved that this hybridizing approach has the potential of improving prediction performance since it has more significant accuracy and leads to smaller errors contrasted with other models. Likewise, the advantages of the hybrid approach were also verified by many studies [18][19][20]。.…”

Section: Literature Reviewmentioning

confidence: 99%

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Guo

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.

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“…In line with this, in the field of technical issues of household energy, many studies are being conducted mainly on energy management using digital technologies. The topics of the literature dealing with the technical issues of household energy consumption include the Home Energy Management System (HEMS) [17][18][19] and the improvement of the accuracy of predicting household energy consumption [20][21][22][23][24][25][26].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Research on improving the accuracy of predicting household energy consumption includes improving the prediction accuracy using machine learning [20][21][22][23][24] and improving prediction accuracy using linear regression models [25,26]. Zhang et al [27] noted that predicting energy consumption is helpful for power demand management, utility companies supply, and demand plans.…”

Section: Literature Reviewmentioning

confidence: 99%

Academic Topics Related to Household Energy Consumption Using the Future Sign Detection Technique

Kim

Park

2021

Energies

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With the emergence of new technologies and policies to transition to clean energy, the household energy consumption sector is also changing. In response to policy, environmental, and technical changes, researchers need to find out what significant issues are related to household energy consumption, and comprehensively analyze which issues are likely to attract attention in the future to contribute to research in the household sector. Based on the abstracts of academic papers published between 2011 and 2020, this study uses probabilistic topic modeling to increase understanding of academic issues in the household energy consumption sector and statistically reviews changes in issues over time. As a result of the analysis, topics related to digitalization and renewable energy, such as microgrid system, smart home, residential solar power generation systems, and non-intrusive load monitoring (NILM), belonging to Strong signals, are being actively studied. Weak Signals, which can attract attention in the future, are included in discussions on coal energy consumption, air pollutant emissions, energy poverty, and energy performance evaluation. The analysis results show that carbon neutrality, such as decarbonization and fossil energy consumption reduction, is expanding to research in the household energy consumption sector.

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RETRACTED ARTICLE: ANN and fuzzy based household energy consumption prediction with high accuracy

Cited by 16 publications

References 37 publications

An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Academic Topics Related to Household Energy Consumption Using the Future Sign Detection Technique

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