Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

“…Additionally, this information can be used to estimate/predict various building loads, which are necessary for the optimal control of HVAC systems [45]. Finally, occupant identity and location could be used to provide personalized/customized indoor environments to occupants to maximize their satisfaction while optimizing energy efficiency [46].…”

“…These sensors comprise temperature sensors, relative humidity sensors, pressure sensors, illuminance sensors, daylight sensors, and air quality sensors [78]. Such sensors are widely used in centralized or distributed predictive control schemes for building energy applications and demand response [46,84,86,87,92,[107][108][109].…”

“…Outlier detection, PCA 25 Zhao et al [211] Chiller Virtual sensor Virtual fouling monitor sensors 26 Li et al [212] Chiller Sensor fault NA 27 Yan et al [213] AHU Sensor fault NA 28 Wang et al [214] VAV terminal Sensor fault NA 29 Yan et al [215] Chiller Feature selection from available sensors Feature selection with back-tracing sequential forward feature selection 30 Sun et al [216] Chiller Sensor data analysis/mining, sensor fault Data fusion 31 Hu et al [217] Chiller Sensor data analysis/mining, sensor fault Sensitivity of chiller sensor fault detection-based on PCA 32 Dey and Dong [218] AHU Sensor fault NA 33 Yang et al [219] AHU Sensor fault NA 34 Padilla and Choiniere [220] AHU Sensor fault NA 35 Kim et al [221] IAQ Sensor fault validation, PCA on sensor Sensor validation 36 Li et al [222] AHU Feature selection from available sensors NA 37 Kim and Braun [223] Chiller Virtual sensor NA 38 Shahnazari et al [224] VAV terminal Sensor fault, sensor data analysis/mining Sensor-fault-tolerant control 39 Wang et al [225] Chiller Feature selection from available sensors NA 40 Li et al [226] Outdoor unit of VRF Additional and built-in/existing sensors, sensor data analysis/mining Data mining using only built-in/existing sensors 41 Fernandez et al [227] AHU Sensor fault NA 42 Karami and Wang [228] Chiller Feature selection from available sensors, sensor fault NA Najafi [229] AHU Additional and built-in/existing sensor Sensor network architectures not necessarily designed solely for diagnostic purposes 44 Dey et al [230] HVAC terminal unit Sensor layout/location Impact of lacking sensor location 45 Kim and Braun [231] Chiller Virtual sensor NA 46 Pourarian et al [232] FCU Sensor fault FDD algorithm adap...…”

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

“…Conventional gradient descent [20] approach has also been investigated to determine the optimal set of MPC parameters based on process repeatability and pre-defined time-domain specifications. Formulation of the cost function, which has to be minimized to determine the MPC parameters, is a complex process, and several attempts have been reported in the literature [21][22][23]. Customization of this cost function and selection of optimization techniques have brought in a wider variant of MPC tuning strategies.…”

“…The cost functions are formulated as per the available plant knowledge and also using the economic aspects. Maximization of comfort and energy saving [21] are achieved by weight tuning in building thermal/heating, ventilation, and air conditioning (HVAC) [22] control system. Minimization of energy consumption in various domestic appliances like the water heater, air conditioner, and refrigerator had been addressed by weight adjustment in the MPC.…”