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MyEMS Energy Management System

website·Indexed 31 days ago

https://myems.cn/

An open-source, AIOT-driven energy management system. Documentation includes installation guides for Windows, Linux (Ubuntu, Debian, CentOS, RHEL, openEuler, Anolis OS, Kylin, SUSE, Fedora), FreeBSD, and Raspberry Pi, as well as Docker and Podman deployment. It provides API specifications, database design architecture, administrator instructions for energy category management, and upgrade paths from v6.7.0 to v6.8.0.

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What's inside MyEMS

  1. Overview of Myems Energy Management System

    Myems is an integrated service platform utilizing cloud computing, IoT, big data, and AI to provide quantitative energy management solutions. It covers the entire cycle of energy purchase, consumption, and resale. The system is designed for a wide range of energy-using units including commercial complexes, hospitals, schools, factories, and urban infrastructure. Key capabilities include energy consumption monitoring, greenhouse gas emission measurement, carbon reduction verification, and digital twin mapping of equipment and services.

  2. MyEMS System Architecture Overview

    MyEMS is an AIOT-driven energy management platform utilizing cloud computing, IoT, and big data. It consists of four primary components: energy data collection (supporting multiple protocols), data storage (using high-performance in-memory databases), data calculation/analysis, and data visualization (accessible via Web, mobile apps, and WeChat mini-programs).

  3. Understand the purpose and use cases of MyEMS

    MyEMS is an Energy Management System (EMS) designed to help organizations establish energy policies, objectives, targets, and action plans. It is specifically intended for environments that require precise monitoring and management of energy usage to optimize processes, improve efficiency, and reduce operating costs.

    Common suitable scenarios include:

    • Factories: Monitoring production line energy consumption to optimize processes and reduce costs.
    • Office Buildings: Tracking energy use for HVAC (air conditioning) and lighting to improve efficiency.
    • Schools: Managing energy consumption across teaching buildings and dormitories for better resource planning.
    • Commercial Complexes: Monitoring shopping malls, hotels, and office buildings to lower operating expenses.

  4. Understand energy consumption aggregation database (Myems_energy_db)

    The Myems_energy_db stores aggregated energy statistics generated by the myems-aggregation service. Data is organized by time granularity (hourly, daily, monthly, yearly) and categorized by object type, direction (input/output), and classification (category/item).
    Naming Convention: tbl_{Object Type}_{Direction}_{Classification}_{Time Granularity} 

Example Object Types: meter, equipment, combined_equipment, space, store, tenant, shopfloor
Example Directions: input, output
Example Classifications: category, item
Example Granularities: hourly, daily, monthly, yearly

  5. MyEMS Technical Architecture and Interfaces

    MyEMS provides three primary interfaces for interaction and management:

    • RESTful API: Developed in Python; provides interfaces for system configuration, energy reporting, and Excel exports.
    • Web UI: Developed in ReactJS; used for energy data analysis.
    • Admin UI: Developed in AngularJS (version 1.x); used for system configuration management.

  6. Deploy AI Skills for autonomous energy analysis

    An AI Skill is a standardized business capability unit in MyEMS. It orchestrates multiple atomic tool calls via MCP or Function Calling and incorporates energy domain rules to perform multi-step professional analysis autonomously.

  7. MyEMS Database Architecture and Configuration

    The MyEMS energy management system utilizes a distributed database architecture designed for data separation, read-write separation (using time series formats for historical data), and horizontal scaling. All databases must be configured with the following standards:

    • Character Set: utf8mb4 (supports full UTF-8, including emojis)
    • Sorting Rule: utf8mb4_unicode_ci
    • Storage Engine: InnoDB (supports transactions and foreign keys)

  8. Manage specialized energy assets in MyEMS Enterprise

    MyEMS Enterprise Edition offers dedicated monitoring and operation platforms for various energy infrastructure types:

    • Energy Storage Stations: Monitoring and O&M platform for storage systems.
    • Photovoltaic (PV) Stations: Monitoring and O&M platform for solar power systems.
    • Wind Farms: Monitoring and O&M platform for wind power systems.
    • Microgrids: Management of interconnected PV, storage batteries, loads, grids, and generators.
    • Virtual Power Plants (VPP): Interconnection and aggregation of distributed energy technologies.

  10. Energy Analysis and Reporting Capabilities in MyEMS

    MyEMS provides comprehensive energy monitoring and analysis tools, including:

    • Overview Page: Tracks monthly total energy consumption, costs, standard coal equivalent, CO2 emissions, and time-of-use electricity consumption.
    • Data Analysis: Supports Year-on-Year (YoY), Month-on-Month (MoM), and custom ratio comparisons.
    • AI Analysis: Provides energy-saving and operation/maintenance suggestions powered by DeepSeek and OpenAI APIs.
    • Meter Analysis: Detailed analysis for energy consumption, carbon emissions, costs, and trends, filterable by spatial hierarchy and time scales.
    • Balance Analysis: Compares main meters against sub-meters to calculate differences and percentage variances.
    • Export: Supports exporting data to native Excel files (including charts and tables), rather than simple CSVs.

  11. Prerequisites for installing MyEMS on Raspberry Pi

    To deploy MyEMS, you need the following hardware and software environment:

    • Hardware: Raspberry Pi 5 or Raspberry Pi 4 Model B
    • OS: Raspberry Pi OS (64-bit), specifically a port of Debian Bookworm (Released 2024-07-04)

  12. Understand the MyEMS Online Demo environment

    The MyEMS online demo simulates a low-carbon city environment with a total building area of 1 million square meters. The simulation includes 179 meters, 284 data points, and 18 different business format subsystems. The infrastructure is deployed on an Alibaba Cloud Container Service Kubernetes cluster, utilizing on-site data collectors for data collection and upload.