Industry Energy Consumption Prediction Using Data Mining Techniques

Sathishkumar, V E; Lim, Jihwan; Lee, Myeongbae; Cho, Kyeongryong; Park, Jang-Woo; Shin, Chang-Sun

doi:10.21742/ijeic.2020.11.1.02

Cited by 11 publications

(6 citation statements)

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“…[6] In kitchen appliances, the gas sensor will detect the presence of gas and send an alert to our Gmail account; it can also send an alert without Wi-Fi if the data is stored in the cloud. [7] The authors have demonstrated a basic application of Raspberry Pi in smart things control via the Internet (E-mail) in a Raspberry Pi-based interactive home automation system via Email, in which the topic of the obtained e-mail is read by the proposed methodology and the process reacts to the respective guidelines. [8] Home automation systems are built to automate operations such as remote control of home appliances.…”

Section: Literature Surveymentioning

confidence: 99%

IoT based security and privacy implementation in smart home

Rajasekhar,

Thanusha,

Jyothi

et al. 2024

ACE

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Internet-of-Things technology is being increasingly important in our daily lives. As IoT technology evolved, IoT devices face a data protection hazard, particularly smart home IoT gateway devices, which became evident. The demand for a low-cost, secure smart home gateway device or router among smart home users. The problem is that as the internet of things (IoT) becomes more ubiquitous, there is a growing need to simplify wireless network control mechanisms. Because data collecting and the process includes processes such as monitoring, judging, and controlling are all involved in IoT, the control mechanism is challenging to simplify. Many internets of things technology offer memory and communication capabilities, and are easily vulnerable to hacking, due to the mobile software available at the tip of one's fingers to operate the linked gadgets to the web. In the Internet of Things, secure data transfer is always a concern. To increase safety in IoT and wireless networks, the current study introduces a unique RSA-based method, as well as the AES algorithm and the lightweight protocol message queue telemetry transport (MQTT).

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Section: Literature Surveymentioning

confidence: 99%

IoT based security and privacy implementation in smart home

Rajasekhar,

Thanusha,

Jyothi

et al. 2024

ACE

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“…The study conducted by Sathishkumar et al [16] in 2020 focuses on using data mining techniques to predict energy consumption in the steel industry. The study is centered around the use of data from the DAEWOO steel company, which includes current reactive power, carbon dioxide emission levels, power factor, and load types.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Global Energy Consumption Through Data Mining Techniques

Rahman,

Abahussin,

Alghamdi

et al. 2024

IJDNE

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With the explosion of the global population and technological progress, the electricity demand has skyrocketed. To ensure a consistent flow of power, it's essential to accurately predict energy usage ahead of time. Failure to do so could lead to potential outages and disrupt our daily lives. This research reviews previous research in the field of using data mining techniques to analyze electricity consumption data, optimize energy performance of buildings, and predict energy consumption in various industries. The study also aims to uncover patterns, correlations, and rules in electricity consumption worldwide using data mining techniques. The analysis is performed using various data mining techniques, such as simple K-Means and Expectation Maximization (EM). This selection is based on their prominent applications for similar problems in literature. The simple K-Means and EM algorithms showed successful outcomes on the dataset, which is evident in the clustering plots. Further, the performance of the Hierarchical Clustering algorithm was not up to the desired standard. This is probably due to the nature of the available dataset. These outcomes of the analysis will provide a valuable resource for decision-makers and stakeholders in the energy sector, as it will provide a deeper understanding of electricity consumption patterns and trends. This could lead to a sustainable future.

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“…Selain itu, penelitian lain mengangkat judul model prediksi konsumsi energi yang efisien untuk suatu data analitik bangunan industri di kota pintar dengan menyajikan dan mengeksplorasi model konsumsi energi prediktif berdasarkan teknik penambangan data untuk industri baja skala kecil yang cerdas di Korea Selatan menggunakan variabel seperti lagging dan arus utama daya reaktif, faktor daya lagging dan arus terdepan, emisi karbon dioksida, dan jenis beban [4]. Selanjutnya penelitian asal Australia mengenai prediksi konsumsi energi industri menggunakan teknik data mining oleh [5] yang menyajikan dan mengeksplorasi model prediksi konsumsi energi menggunakan pendekatan data mining untuk industri baja hingga menunjukkan bahwa model Random Forest dapat memprediksi konsumsi energi terbaik dan mengungguli algoritma konvensional lainnya dalam perbandingan.…”

Section: Pendahuluanunclassified

Klasifikasi Konsumsi Energi Industri Baja Menggunakan Teknik Data Mining

Rahayu¹,

Purnama

2022

JTI

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Human needs in fulfilling clothing, food and housing in today's life cannot be separated from the involvement of electrical energy. In several sectors of life, namely the household sector, industry, business, social, government office buildings, and public street lighting, electricity is needed. The energy consumption industry sector is relatively higher than other sectors, so it is necessary to control energy consumption, especially in the industrial sector. As a result, for a nation or region, forecasting the use of electrical energy becomes urgent and crucial. Research on this issue has emerged from various countries, for example, research from Korea on energy consumption prediction models for smart factories using a data mining algorithm that introduces and explores the steel industry energy consumption prediction model by producing the best model, namely Random Forest with an RMSE value of 7.33 in the test set. In addition, another study raised the title of an efficient energy consumption prediction model for an analytical data of industrial buildings in a smart city by presenting and exploring a predictive energy consumption model based on data mining techniques for a smart smallscale steel industry in South Korea using variables such as lagging and current. main reactive power, lagging power factor and leading current, carbon dioxide emission and load type. Research from Australia is also not left behind, discussing the prediction of industrial energy consumption using data mining techniques which presents and explores energy consumption prediction models using a data mining approach for the steel industry to show that the Random Forest model can best predict energy consumption and outperform other conventional algorithms in comparison. This study presents a classification of energy consumption in the steel industry, in order to know the pattern of using light loads, medium loads, and maximum loads using data mining techniques on public data that is already available on this matter, with the aim that energy users in the steel industry are wiser in using energy because you already know the pattern of each load. The methods used include Random Forest, Decision Tree, Naïve Bayes and Artificial Neural Networks resulting in accuracy of 91.13%, 90.50%, 70.97% and 75.56%, so that the classification method is the most suitable for use. In classifying industrial energy consumption on the steel industry energy consumption dataset, Random Forest.

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Industry Energy Consumption Prediction Using Data Mining Techniques

Cited by 11 publications

References 0 publications

IoT based security and privacy implementation in smart home

IoT based security and privacy implementation in smart home

Predicting Global Energy Consumption Through Data Mining Techniques

Klasifikasi Konsumsi Energi Industri Baja Menggunakan Teknik Data Mining

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