Design and Modeling of an AI-Powered Industrial Maximum Demand Controller With Web Service Interface

Author:

Buhari Abudhahir1,Hajamydeen Asif Iqbal2ORCID,Nyamasvisva Tadiwa Elisha1ORCID,Salome Selvi3

Affiliation:

1. Infrastructure University, Kuala Lumpur, Malaysia

2. Management and Science University, Malaysia

3. Protasco Bhd, Malaysia

Abstract

Industrial facilities face challenges in managing energy consumption and minimizing peak demand charges. Managing maximum demand is a critical aspect of energy management in industrial facilities. The proposed MDC is based on a predictive analysis method using time-series models such as long short-term memory (LSTM) and XGBoost. However, this chapter also discusses the pros and cons of TCN and k-NN model oriented models. Further, this chapter discusses development and implementation of the proposed MDC as a web service. Finally, development of a user-friendly web interface for dataset uploads, system configuration, and alerts are explored.

Publisher

IGI Global

Reference41 articles.

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3. Bimenyimana, S., & Asemota, G. N. O. (2018). Traditional Vs Smart Electricity Metering Systems: A Brief Overview. Journal of Marketing and Consumer Research. https://www.semanticscholar.org/paper/Traditional-Vs-Smart-Electricity-Metering-Systems%3A-Bimenyimana-Asemota/c403fb7adaab7cb24697c287d0c1701dd0ff78de

4. Automated energy meter using WiFi enabled raspberry Pi

5. Dynamics between Power Consumption and Economic Growth at Aggregated and Disaggregated (Sectoral) Level Using the Frequency Domain Causality

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