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Pandarasamy Arjunan

Pandarasamy Arjunan
Berkeley Education Alliance for Research in Singapore (BEARS) Limited · SinBerBest

PhD

About

26
Publications
11,581
Reads
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339
Citations

Publications

Publications (26)
Preprint
Full-text available
Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding few million data points every day. This data can be leveraged to discover the underlying loads, infer their energy consumption patterns, inter-dependencies on environmental factors, and the building's operational...
Article
Full-text available
Building energy use benchmarking is the process of measuring the energy performance of buildings, relative to their peer group, for creating awareness and identifying energy-saving opportunities. In this paper, we present the design and implementation of BEEM, a data-driven energy use benchmarking system for buildings in Singapore. The peer groups...
Article
Full-text available
We investigate the role of explainable Artificial Intelligence (XAI) for building trust in data-driven fault detection and diagnosis (FDD). We examine use cases for XAI-FDD on a building in Singapore that has six chillers.
Article
Full-text available
Many energy performance analysis methodologies assign buildings a descriptive label that represents their main activity, often known as the primary space usage (PSU). This attribute comes from the intent of the design team based on assumptions of how the majority of the spaces in the building will be used. In reality, the way a building’s occupants...
Article
Full-text available
This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more mete...
Article
Full-text available
Building energy performance benchmarking has been adopted widely in the USA and Canada through the Energy Star Portfolio Manager platform. Building operations and energy management professionals have long used this simple 1–100 score to understand how their building compares to its peers. This single number is easy to use but is created by potentia...
Article
Full-text available
In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meter...
Preprint
Full-text available
This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters are collected from 19 sites across North America and Europe, and they measure elect...
Preprint
Full-text available
In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meter...
Preprint
Full-text available
Building energy performance benchmarking has been adopted widely in the USA and Canada through the Energy Star Portfolio Manager platform. Building operations and energy management professionals have long used a simple 1-100 score to understand how their building compares to its peers. This single number is easy to use, but is created by inaccurate...
Article
Full-text available
Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand contr...
Preprint
Full-text available
Many energy performance analysis methodologies assign buildings a descriptive label that represents their main activity, often known as the primary space usage (PSU). This attribute comes from the intent of the design team based on assumptions of how the majority of the spaces in the building will be used. In reality, the way a building's occupants...
Article
Buildings are responsible for 30–40% of the anthropogenic greenhouse gas emissions and energy consumption worldwide. Thus, reducing the overall energy use and associated emissions in buildings is crucial for meeting sustainability goals for the future. In recent years, smart energy meters have been deployed to enable monitoring of energy use data w...
Conference Paper
Buildings are one of the largest energy consumers around the world. Several studies show that degraded and misćonfigured devices waste upto 30% of energy in commercial buildings. In this paper, we propose Collect, Compare, and Score (CCS), a generic anomaly detection method that can be used across buildings following different energy usage p...
Conference Paper
Full-text available
Anomaly detection is an important problem in building energy management in order to identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies from the genuine changes in energy consumption due to seasonal variations and changes in personal settings such as holidays. One of the important drawbacks of existing an...
Conference Paper
Full-text available
Buildings, with their different subsystems interacting with diverse occupants, constitute a complex Cyber-Physical-Human infrastructure. Monitoring and controlling this complex ecosystem is essential both for efficient and optimized operations of building subsystems and for influencing the occupant behavior. A critical enabling technology in this c...
Conference Paper
Full-text available
Electricity demand prediction is important for several real world applications such as Demand Response (DR) program for peak demand management. For utilities with many customers, learning a best fit baseline for every consumer may be time consuming. We propose E-Adivino: an electricity forecasting framework that first clusters customers based on th...
Conference Paper
Full-text available
Towards the realization of smart building applications, buildings are increasingly instrumented with diverse sensors and actuators. These sensors generate large volumes of data which can be analyzed for optimizing building operations. Many building energy management tasks such as energy forecasting, disaggregation, among others require complex anal...
Conference Paper
Full-text available
Buildings are one of the largest consumers of electricity. Dominant electricity consumption within the buildings, contributed by plug loads, lighting and air conditioning, can be significantly improved using Occupancy-based Building Management Systems (Ob-BMS). In this paper, we address three critical aspects of Ob-BMS i.e. 1) Modular sensor node d...
Conference Paper
Full-text available
The archaic centralized software systems, currently used to manage buildings, make it hard to incorporate advances in sensing technology and user-level applications, and present hurdles for experimental validation of open research in build-ing information technology. Motivated by this, we — a transnational collaboration of researchers engaged in de...
Conference Paper
In this work, we propose a scalable, end-to-end, fine-grained and low-cost resource (electricity) management system for buildings. Our proposed work is in the direction of using low cost sensing approaches (including existing sensory information) and designing middleware for end-to-end management.
Article
Full-text available
Ubiquitous availability of cellular network and cheap mobile phones have made them promising sensing platforms for various application domains including healthcare, environment, and astronomy among others. However, existing mobile phone platforms, including even smartphones, provide limited in-built sensing capabilities and lack standard interfaces...

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Project (1)
Project
Crowdsource the most accurate long-term energy prediction models for buildings