Anomalous STLF for Indonesia power system using Artificial Neural Network

Mulyadi, Yadi; Farida, L.; Abdullah, Ade Gafar; Rohmah, K. A.

doi:10.1109/ticst.2015.7369331

Cited by 3 publications

(2 citation statements)

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“…This leads buildings becoming the largest energy consumers worldwide and motivates people to find accurate and easy ways to monitor and control their electricity. The efficacy of monitoring actual electricity load in buildings not only provides convenience in controlling energy savings but also able to accurately manage load balancing to avoid outage and further damages as power overload, over or under voltage are still common problems on the consumers' side [5], [6]. If this occurs for a long time, it may potentially damage not only the network installation itself but also consumer equipment or even building fire.…”

Section: Introductionmentioning

confidence: 99%

“…This article aims to show how a simple, real-time, and high-resolution energy monitoring and analytics using open IoT platforms is constructed as a monitoring model for smart buildings. This model should be able to visualize either short or long term fluctuation of loads, voltage, current, frequency, and apparent and real power as they are common abnormality of electrical AC building's installation especially in Indonesia [6] and therefore energy savings can be controlled, and potential failure and damages can be anticipated.…”

Section: Introductionmentioning

confidence: 99%

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A Simple Real-Time Energy Analytics Model for Smart Building Using Open IoT Platforms

Nasar¹,

Setyawan²,

Faruq³

et al. 2019

indones.j.electron.telecommun.

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Monitoring energy in buildings can ease us to have a better understanding of electricity consumption patterns to support efficiency and avoid potential damages. However, indoor installations are mostly unmonitored because their panel meters are usually difficult to access. Yet, indoor maintenance tends to be more difficult since the cables are inside the wall, ceiling, or concrete. Internet of Things and big data analytics can be used to track electricity usage either in residential, commercial, or industrial buildings. This paper presents how a simple real-time energy data analytics was built at a low cost and high accuracy to inspect energy fluctuations, anomaly, and its significant pattern. We proposed 3 layers of architecture namely acquisition, transportation, and application management. An electronic module named PZEM004T was used to sense voltage, current, and other electrical parameters. Through a microcontroller ESP8266, the data was processed and sent it to an application layer via an existing wireless network. The actual and historical data of electricity were visualized on high-resolution graphs. The experiment was conducted at our office building. The experimental results showed that data of electrical energy usage can be captured close to realtime and power anomaly and pattern can be figured. Performance and functionality testing showed acceptable use of this system with more than 99% accuracy. This system is intended to empower building managers in evaluating the electrical network balance as well as anticipating damage due to overload, overvoltage, and voltage drop. If this model is widely implemented it will produce big data that is useful for advanced analysis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%