Data-Driven Smart Buildings

Semantic Application in Building Automation: Government Offices in Hong Kong

The government office complex (GOC) located in Kowloon, Hong Kong consists of two office towers (16 and 18 stories respectively), totalling over 98,000 m²of space for government bureaus and departments. The complex has a central chiller plant located in the basement that supplies chilled water to the towers’ air conditioning systems through a network of pipes. The chiller plant contains three 3,517 kW water-cooled chillers (two operational, and one backup), along with one 3,517 kW heat recovery chiller for added cooling and heating capacity. The Electrical and Mechanical Services Department (EMSD) developed AI models for cooling-load prediction and overall chiller system’s coefficient of performance (COP) evaluation by means of historical data from the building management system (BMS) and real-time data collected from internet of things (IoT) sensors.

Project Information

Location

Kowloon, Hong Kong

Building Typology

Miscellaneous, including office buildings

Technology Installed / Proposed

An AI-enabled digital twin platform utilising multivariate regression, ensemble learning models and a semantic data model was implemented to optimise chiller performance and operations.

Data Availability

Data access will be considered on a case-by-case basis for collaborative research.

Status

Operational - Results Available

Project Aim

EMSD manages over 8,000 facilities across the Hong Kong Special Administrative Region (HKSAR) Government portfolio. In response to the Climate Action Plan 2050 [1] established by Hong Kong SAR Government and a nationwide campaign to accomplish carbon neutrality before 2050, this project aimed at reducing electricity consumption in all Government office complexes by optimising energy efficiency.

A review of energy usage data of the Kowloon government office complex revealed inefficient HVAC system operations as a major electricity consumer. To address this issue, the EMSD’s project team collected and pre-processed building’s electrical and mechanical (E&M) datasets to analyse operational patterns and train an ensemble learning model to predict the cooling load demand, enabling the designed system to centrally optimise chiller functions. Specifically, the project team was tasked with:

Optimising the energy efficiency of HVAC systems in Hong Kong’s Government facilities.
Evaluating the portability and scalability of semantic models used in buildings.

To achieve this goal:

An ensemble learning model was trained to predict cooling load demand of the building and adopted multivariate polynomial regression to estimate the COP of each chiller.
The final model was adopted to generate an optimal operating sequence for the chillers that could provide the highest average seasonal COP over the next 7-day cooling load predictions.
A semantic approach was harnessed to visualise the relationship among components in the chiller plant.

Additional Information

C.W. Chen, C.C. Li, C.Y. Lin. (2020). Combine clustering and machine learning for enhancing the efficiency of energy baseline of chiller system. Energies, 13(17), 4368. https://doi.org/10.3390/en13174368

The Hong Kong Energy End-use Data 2023.

The RDF standard by W3C.

Dashboard in real-time monitoring Daily Mode

Dashboard in real-time monitoring Disaster Mode

Dashboard in real-time monitoring Energy Mode

Prediction accuracy for different XGBoost (eXtreme Gradient Boosting) iterations

Architectural overview of the semantic data platform

Knowledge graph of the Semantic AI Model connecting Building Information Modelling

For more information on the Case Study

Contact Person: Patrick So, Calvin Leung

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