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Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application

Alessandro Massaro^1,2, Giuseppe Starace¹

¹Università LUM "Giuseppe Degennaro", S.S. 100-km 18, Casamassima, 70010 Bari, Italy.

Sensors (Basel, Switzerland)

|July 9, 2022

Summary

This review synthesizes energy metering systems, focusing on sensors, technologies, and advanced measurement approaches. It introduces models for estimating Key Performance Indicators (KPIs), incorporating Artificial Intelligence (AI) for complex energy networks.

Keywords:

KPIs energy control strategies energy systems monitoring

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Area of Science:

Energy Systems Engineering
Measurement Science
Artificial Intelligence

Background:

Complex energy monitoring and control systems are crucial across diverse application fields.
Existing research covers various approaches, sensors, and technologies for energy metering.
A systematic review is needed to consolidate knowledge on energy metering issues.

Purpose of the Study:

To systematically review energy metering systems, focusing on sensors, technology selection, characterization, advanced measurement methods, and Key Performance Indicator (KPI) setup.
To provide models for KPI estimation and highlight design criteria for complex energy networks.
To explore the integration of Artificial Intelligence (AI) for developing innovative complex KPIs.

Main Methods:

Systematic literature review of energy metering systems.
Analysis of sensor technologies and their application-specific characterization.
Development of models for KPI estimation and AI-integrated KPI formulation.
Utilizing graph models for architecture representation.

Main Results:

Identification of key aspects in energy metering: sensors, technology choices, advanced measurement approaches, and KPI definition.
Presentation of models for KPI estimation, applicable to complex energy network design.
Demonstration of AI integration for creating complex KPIs from basic variables and KPIs.
Validation of modeling complex KPIs using graph-based architectural representations.

Conclusions:

The study provides a comprehensive overview of energy metering systems, offering valuable insights for system design and simulation.
The proposed models facilitate performance prediction and the development of advanced KPIs, particularly through AI integration.
The findings enable detailed simulation of energy systems and support the creation of innovative, application-specific KPIs.