Fuzzy linguistic knowledge based behavior extraction for building energy management systems

Abstract

Abstract-Significant portion of world energy production is consumed by building Heating, Ventilation and Air Conditioning (HVAC) units. Thus along with occupant comfort, energy efficiency is also an important factor in HVAC control. Modern buildings use advanced Multiple Input Multiple Output (MIMO) control schemes to realize these goals. However, since the performance of HVAC units is dependent on many criteria including uncertainties in weather, number of occupants, and thermal state, the performance of current state of the art systems are sub-optimal. Furthermore, because of the large number of sensors in buildings, and the high frequency of data collection, large amount of information is available. Therefore, important behavior of buildings that compromise energy efficiency or occupant comfort is difficult to identify. This paper presents an easy to use and understandable framework for identifying such behavior. The presented framework uses human understandable knowledge-base to extract important behavior of buildings and present it to users via a graphical user interface. The presented framework was tested on a building in the Pacific Northwest and was shown to be able to identify important behavior that relates to energy efficiency and occupant comfort.

Potential of Saving Energy Using Advanced Fuzzy Logic Controllers in Smart Buildings in Subtropical Climates in Australia

“…In addition the controlled building consumed energy using conventional PID Controllers can be estimated using equation 6.…”

“…A vast number of control strategies and techniques have been proposed, and searched in the past two decades due to rapid development of computers, controllers, sensors and communication techniques [3]. These research activities were focused on controllers' technical performance including control strategies and communication protocols [4][5][6][7]. Australian subtropical regions' buildings' occupancy based control strategies (Fuzzy based) are not included in its management systems.…”

Neural Network based downscaling of Building Energy Management System data

“…Similarly, in the United States, buildings account for 40% of the total energy consumption [1]- [3]. Due to the high energy consumption, buildings are also one of the major contributors to green house gas production [4].…”

“…Thus, in order to maintain and improve both energy efficiency and occupant comfort, close monitoring of the building environment is necessary [2], [3], [14]. To achieve this, modern buildings rely on thousands of sensors strategically placed throughout the building [15].…”