Building Energy Management System Data Research Articles

Assessing unregulated electricity consumption in a case study university

Energy consumption in buildings varies considerably depending on type and usage. However, monitoring and regulation of electrical power consumption due to user-related activities are not presently embedded in current English Building Regulations. These energy uses include lifts, server rooms, and small power loads, and are collectively referred to as unregulated energy usage. As part of a larger study of unregulated energy usage in university campus buildings, this paper describes the development and demonstration of a methodology for quantifying unregulated power usage using Building Energy Management System data for a case study university. The methodology was applied to four different buildings. This approach was applied to a Chemical Laboratory, a Physical Science Laboratory, a Library and an Administration Office. The results obtained revealed that electrical consumption was consistently much higher than traditional benchmarks suggest as being normal, particularly within the laboratories. Additionally, within the specific unregulated electricity case study, four rooms in the Physical Science Laboratory indicated very high unregulated electricity usage figures, averaging at 89 kWhm−2 per annum across just four rooms. Practical application: Unregulated energy research, within the Higher Education Sector, is undeveloped. Previous literature focuses on specific areas of unregulated energy, such as equipment usage. However, analysis based on different room types has not been completed on a wider scale. Hence, there is a gap in the current research. This research shows analysis of unregulated energy in different university buildings. It develops a methodology to further understanding on this undeveloped area. Additionally, it advises that unregulated energy must be calculated during a building’s design stage. By using a selection of case studies, this research furthers understanding on the overall effects of unregulated energy.

Development of a model predictive control framework through real-time building energy management system data

Over the past several years, studies have been conducted on the model predictive control (MPC), which has analyzed the amount of savings through model-based predictive control. As its result is dependent on the precision and accuracy of the model, minimizing the errors due to arbitrary events caused by the occupants as well as uncertain data input is important. In this study, to address these errors, real-time building energy simulation was conducted in accordance with which energy consumption could be predicted and a MPC framework was established for control. To use the building energy management system data, co-simulation was implemented based on the Building Controls Virtual Test Bed (BCVTB) as it allows for a real-time simulation based on the external data input. The real-time predicted and measured energy consumption values were compared to the statistical indices such as the hourly Mean Bias Error and Coefficient Variation of the Root-Mean-Squared Error with acceptable values of −0.7% and 19.1%, respectively. As a case study, an enthalpy control algorithm with the real-time monitoring data was successfully implemented producing the damper position of the air handling unit.

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

Building Energy Management Systems (BEMSs) are essential components of modern buildings that are responsible for minimizing energy consumption while maintaining occupant comfort. However, since indoor environment is dependent on many uncertain criteria, performance of BEMS can be suboptimal at times. Unfortunately, complexity of BEMSs, large amount of data, and interrelations between data can make identifying these suboptimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD)-based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior; and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm-based BEMS. Six different scenarios were tested, and the presented Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more) than the alarm based BEMS. Furthermore, the Fuzzy-ADLD method identified cases that were missed by the alarm-based system, thus demonstrating potential for increased state-awareness of abnormal building behavior.

Improved measurements for better decision on heat recovery solutions with heat pumps

The aim of the study was to show the need for performance documentation, monitoring, and data integration during the lifetime of an energy system to achieve proper decision making. An improved measurement approach for heat pump performance was introduced. This approach was developed by integrating manufacturer and building energy management system data. Direct and indirect measurements were combined into fused measurements. This heat pump estimation approach was tested on a substation where integrated heat pumps supported a building energy supply system. Two approaches were assessed for exhaust air heat recovery: within the air handling unit and by using heat pumps. The results showed that improved measurements were cost-effective and highly reliable in the decision making. The exhaust air heat recovery heat pump was the favorable solution when district heating price was 60% above the electricity price and in the case when electricity was produced by renewable energy sources.