T/Output Pit NH3 concentration, Area temperature, Pit temperature, Area humidity, Pit humidity, Pig activities, Pit

T/Output Pit NH3 concentration, Area temperature, Pit temperature, Area humidity, Pit humidity, Pig activities, Pit fan-E speed, Pit fan-W speed, Area fan 14 ‘, Area fan 20 ‘, and Pig manure Time of day, barometer level Tasisulam Biological Activity stress (hPa), sea level pressure (hPa), temperature ( C), relative humidity , wind speed (knots), wind path (knots), Pasquill atmospheric stability class, global solar radiation (j. cm2 ) and outside pollutant concentrations CO2 , VOC, humidity, temperature, light amount, and fine dust Indoor PM2.five GYKI 52466 MedChemExpress concentration Indoor PM2.five and PM10 concentration, indoor temperature, relative humidity, indoor CO2 concentration xt (existing input) Internal and external temperature, internal RH, date and time Final results ANFIS, BP and MLRM results in summer season and winter MSE = 0.0047 and 0.002, R2 = 0.6483 and 0.6351; MSE = 0.0137 and 0.0042, R2 = 0.6066 and 0.5543; MSE = 0.0174 and 0.0660, R2 = 0.5957 and 0.702.Xie et al. [314]CommercialANFIS, BP, MLRMNHChalloner et al. [315]OfficeANNNO2 , PM2.Location 1, 2 and three: For NO2 , R2 = 0.854, 0.870, 0.829; For PM2.five , R2 = 0.711, 0.760, 0.770.Ahn et al. [316] Adeleke et al. [317]2017Office ResidentialGated recurrent unit LSTM MLP NNPM2.5 , CO2 , VOCs PM2.Prediction Accuracy: GRU = 84.69 LSTM = 70.13 Precision as much as 0.86, Sensitivity of as much as 0.85. For PM2.5 , R2 = 0.97 For PM10 , R2 = 0.91 For Fungi, R2 = 0.68 RMSE = 29.73 /m3 , MAPE = 29.52 RMSE = 30.99 /m3 , MAPE = 31.10 RMSE = 46.25 ppmLiu et al. [318]ResidentialANNCO2 , PM2.five , and PMLoy-Benitez et al. [319]Waiting roomsDeep RNNPM2.five , PM10 , CO2 , NO2 , CO, NOVanus et al. [320]ResidentialDecision tree regression methodCOSustainability 2021, 13,27 ofTable 7. Cont.Author [Ref] Ha et al. [321] Elhariri et al. [322] Year 2020 2019 Creating Kind Workplace Office Strategy Extended fractional-order Kalman filter Gated recurrent unit Machine learning-based non-parametric forecasting Various linear regression, non-linear ANN Time slicer strategy, PAD process ARIMA IAQ Parameter H2 , NH3 , ethanol, H2 S, toluene, CO, CO2 , O2 CO2 Input/Output CO2 , CO, O2 , H2 , NH3 , ethanol, H2 S, toluene, temperature, humidity Humidity, temperature and CO2 Humidity, temperature, VOCs, PM2.five Final results MSE = 0.8612, 0.39993, 0.7082, 0.5122, 0.6103, 0.6761, 0.4738, 0.4262, 0.3601, 0.3007 RMSE = 4.Fang et al. [323]ResidentialPM2.five , VOCNRMSD = 7.five For O3 : RMSE = 7.four ppb, R2 = 0.78 For CO2 : RMSE = 8.1 ppb, R2 = 0.88 PAD method has a lot more accuracy than time slicer process Mean prediction error = 0 The model have high prediction accuracyMaag et al. [324]Office and residentialO3 , CO2 , VOCO3 , temperature, VOCSchwee et al. [325]OfficeCO2 PM2.5 , PM10 , CO2 , tVOC, formaldehydeCO2 , temperature PM2.five , PM10 , temperature, CO2 , tVOC, formaldehydeXiahou et al. [326]Residential6.four.three. AI in VC The use of artificial intelligence in VC is tabulated in Table 8 [32732]. The input parameters utilised by several researchers will be the orientation from the sun, illuminance levels, glare level, opening of windows, and climate situations, etc. The most-used computational approaches in a variety of studies are Fuzzy rule primarily based, the Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Non-Dominated Sorting Genetic Algorithm (NSGA-II), Genetic Algorithm (GA), Linear Regression (LR), and Support Vector Machine (SVM).Table 8. Summary of AI research studies in VC.Author [Ref] Rodriguez et al. [327] Year 2015 Constructing Form Workplace Strategy Fuzzy rule base VC Parameter Organic and artificial Light Input/Outp.