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Journals / International Scientific and Vocational Studies Journal / 2021 / Cilt: 5 - Sayı: 2

Machine Learning Based Short Term Load Estimation in Commercial Buildings

Mustafa Yasin ERTEN
Nihat İNANÇ
Pages
171–181
Publication Date
2021-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Therefore, the developed artificial neural network-based load estimation algorithm is capable of high accuracy load estimates, and high precision data were obtained for use in the demand side management system

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nowadays, there are many problems with the electricity system, such as increasing consumption, short-time overload during the intra-day, environmental problems caused by fossil fuel, and foreign-source dependency.
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Key Findings & Data:Therefore, it is necessary to meet these increasing energy needs, minimize environmental impacts, and develop cost optimization solutions.
💡
Conclusion & Impact:Therefore, the developed artificial neural network-based load estimation algorithm is capable of high accuracy load estimates, and high precision data were obtained for use in the demand side management system
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Short term load prediction · Machine Learning · Artificial Neural Network

Abstract

Nowadays, there are many problems with the electricity system, such as increasing consumption, short-time overload during the intra-day, environmental problems caused by fossil fuel, and foreign-source dependency. Therefore, it is necessary to meet these increasing energy needs, minimize environmental impacts, and develop cost optimization solutions. In order to meet these requirements, it is necessary for the network to have a more dynamic structure and to have real-time monitoring and control systems. Furthermore, to develop the aforementioned system, it is necessary to estimate the load of the users in the system. Therefore, the developed artificial neural network-based load estimation algorithm is capable of high accuracy load estimates, and high precision data were obtained for use in the demand side management system

Özet

Nowadays, there are many problems with the electricity system, such as increasing consumption, short-time overload during the intra-day, environmental problems caused by fossil fuel, and foreign-source dependency. Therefore, it is necessary to meet these increasing energy needs, minimize environmental impacts, and develop cost optimization solutions. In order to meet these requirements, it is necessary for the network to have a more dynamic structure and to have real-time monitoring and control systems. Furthermore, to develop the aforementioned system, it is necessary to estimate the load of the users in the system. Therefore, the developed artificial neural network-based load estimation algorithm iscapable of high accuracy load estimates, and high precision data were obtained for use in the demand side management system.

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Cite This Article

ERTEN, M. Y., İNANÇ, N. (2021). Machine Learning Based Short Term Load Estimation in Commercial Buildings. International Scientific and Vocational Studies Journal, Cilt: 5 - Sayı: 2, 171–181. https://ajindex.com/articles/769769

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References (10)

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