A review of smart building sensing system for better indoor environment control

Daylight stems solely from direct, scattered and reflected sunlight, and undergoes dynamic changes in irradiance and spectral power composition due to latitude, time of day, time of year and the nature of the physical environment (reflections, buildings and vegetation). Humans and their ancestors evolved under these natural day/night cycles over millions of years. Electric light, a relatively recent invention, interacts and competes with the natural light–dark cycle to impact human biology. What are the consequences of living in industrialised urban areas with much less daylight and more use of electric light, throughout the day (and at night), on general health and quality of life? In this workshop report, we have classified key gaps of knowledge in daylight research into three main groups: (I) uncertainty as to daylight quantity and quality needed for “optimal” physiological and psychological functioning, (II) lack of consensus on practical measurement and assessment methods and tools for monitoring real (day) light exposure across multiple time scales, and (III) insufficient integration and exchange of daylight knowledge bases from different disciplines. Crucial short and long-term objectives to fill these gaps are proposed.

show abstract

“…Self-control of (day-) light How much self-control over sensor control is necessary and desired [219]?…”

Section: Latitudementioning

confidence: 99%

The Role of Daylight for Humans: Gaps in Current Knowledge

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Data sufficiency is the biggest challenge for understanding the nature, diversity and distribution of OB [2,7,112] Collection of OB data is normally high in cost and hard to implement [113][114][115], because of high capita cost of occupancy sensors (including occupancy sensors, indoor environment sensors, and occupant behavior sensors such as appliance sensors) [116]. OB in building appliance operation such as AC or television is easier because the monitoring of appliance energy usage would reflect OB directly.…”

Section: Ob Data and Sharing Protocol Deficiencymentioning

confidence: 99%

A systematic review of occupant behavior in building energy policy

Yan

Azar

et al. 2020

Building and Environment

125

View full text Add to dashboard Cite

Buildings play a dominant role in global efforts towards energy consumption reduction, greenhouse gas (GHG) emission mitigation, as well as global clean energy transition. Building Energy Policies (BEP) improved globally and quickly with a growing number of building codes implemented over the past decade. Occupant Behavior (OB) has significant impacts on building energy performance and occupant comfort, despite often being not well understood and oversimplified in BEPs. This paper highlighted the research needs of properly integrating OB in building energy polices by presenting a literature review to identify the key questions and challenges related to building technical standards and regulations, building information policies, building energy incentives, and policy evaluations and way forward. Challenges and opportunities of OB in BEP are also discussed with respect to technical innovation and digitalization, as well as concerns related to energy efficiency and fairness. There has been growing interests, research and applications in this field, but significant challenges and opportunities still lie ahead. Keywords: Occupant behavior; building energy policy; climate change policy; building energy efficiency;HVAC system 1. Introduction Occupant behavior in building energy policyBuildings play a dominant role in global efforts towards energy consumption reduction, greenhouse gas (GHG) emission mitigation, as well as global clean energy transition. Facing global climate change and national energy security challenges, international agreements, initiatives, and building sector energy policies are providing 2 promising alternatives to the status quo. In recent years, Building Energy Policies (BEP) improved globally and quickly with a growing number of building codes implemented over the past decade. In contrast, global final energy consumption in buildings increased by more than 5% between 2010 and 2017, as energy efficiency gains were outpaced by continued growth in the building sector size and resulting energy service demands [1]. Among various factors, Occupant Behavior (OB) plays an essential role in building energy demand increases and activity growth. In addition, OB has significant impacts on building energy performance and occupant comfort, despite often being not well understood and oversimplified in BEPs due to its stochastic, diverse, complex and interdisciplinary nature [2]. Definition, estimation and intervention of OB become essential research challenges during building energy policy making, implementation and evaluation processes. Real OB US OB China OB EU OB EU regulation China regulation OB in regulation US regulation Country X OB Country X regulation Gap 2 Gap 1 Gap 3

show abstract

“…Several previous studies [17][18][19][20] have investigated localized temperature sensing methodology. Multiple sensors such as wireless sensors have been used instead of one sensor, or existing sensor data has been used to control the indoor environment.…”

Section: Localized Temperature Sensingmentioning

confidence: 99%

“…Recently, artificial intelligence-based techniques are being investigated as a potential means to reduce the need for dense and costly sensor infrastructure that may be needed for accurate prediction of thermal comfort [90]. Research from Kumar et al [91] and Dong et al [20] provide a more in-depth review of indoor air quality (IAQ) and energy sensing.…”

Section: Optimization-based Control For Thermal Comfort and Energy Efmentioning

confidence: 99%

“…Lee and Yik [173] Sensor fault Energy cost impacts of sensor faults 19 Guo et al [174] Feature selection from available sensors Hybrid feature selection, VRF 20 Guo et al [175] Sensor fault, sensor data analysis/mining Senor fault detection, VRF 21 Shi et al [176] Sensor data analysis/mining VRF 22 Li et al [177] Virtual sensor Virtual sensor-based fault indicators for a VRF 23 Verhelst et al [178] Sensor fault Economic impact of persistent sensor and actuator faults 24 Yoon and Yu [179] Sensor calibration Virtual in-situ sensor calibration 25 Shi et al [180] Feature selection from available sensors VRF 26 Yu et al [181] Sensor calibration Indirect virtual calibration method for a supply air temperature in an RTU 27 Kim [182] Virtual sensor Review of virtual sensor, evaluation of sensors, development and assessment of alternative virtual sensors in an RTU 28 Katipamula et al [183] Additional senor, sensor layout/location, sensor fault…”

Section: No Authors Ref Sensor Topics Abstract 18mentioning

confidence: 99%

See 1 more Smart Citation