Actual Weather Data Research Articles

Short-Term Forecasting of Power Consumption of Power Supply Companies Based on the Integration of Technologies of Analytical, Simulation and Expert Systems

Purpose of research is to improve software support and identify regularities in the processes of short-term forecasting of power consumption of power supply companies based on complementary integration of data mining models, system dynamics and expert systems.Methods. The principles of constructing predictive models of power consumption are given. A system analysis has been carried out and an ontological model of the subject area has been built, taking into account the technological and market environment. The classification of forecasting methods has been considered. The features of the information base for short-term forecasting, including data on actual power consumption and weather data, have been described. The requirements for software for making forecasts have been formulated. A block diagram of the system for forecasting power consumption of the market for the day ahead is built based on the complementary integration of data analysis and modeling software.Results. Scenarios for data processing in Loginom have been developed using the Arimax and Neural Network (Regression) processors to build forecasts based on actual power consumption and taking into account meteorological factors. A system dynamics simulation model that allows exploring the influence of meteorological factors (temperature, pressure, precipitation) on power consumption has been developed in Anylogic. Using Wi!Mi mivar constructor of expert systems, the task has been parametrized; indicators, relationships, rules have been set; a logical conclusion of the solution has been obtained.Conclusion. A block diagram of a system for forecasting the market's power consumption for the day ahead has been built. It is based on the analysis of retrospective information on actual power consumption and meteorological factors using data mining methods, system dynamics and expert systems applying Russian Loginom, Anylogic and Wi!Mi software tools.

Datasets of a Multizone Office Building under Different HVAC System Operation Scenarios

This study provides an open-source dataset of the measured weather data, building indoor data, and system data under the different test settings. The test building is the two-story Flexible Research Platform building at the US Department of Energy’s Oak Ridge National Laboratory, in Oak Ridge, Tennessee. Four heating tests and three cooling tests were conducted. The 1-min interval of weather, building indoor data, and system data from each test setting are provided. Actual weather data were collected from a weather station installed on the roof. This paper describes information on the test building and installed sensors, data collection method, and data validation. The provided dataset can be employed to understand HVAC system conditions and building indoor conditions under different HVAC system operations and the performance of building envelope without HVAC system operation using free-floating test data. Additionally, it can be used for empirical validation of the building energy modelling engine.

ClimaSG: A weather generator for crop modelling and water requirements studies

A new stochastic weather generator, ClimaSG, is presented and evaluated under a wide range of climates. The model requires monthly means data as inputs to generate daily weather data. Validation tests revealed that ClimaSG performed well in capturing monthly rainfall variability but showed some limitations for generating extreme temperatures. Solar radiation, air humidity, and wind speed were reproduced acceptably. The weather generator was particularly accurate in predicting mean and extreme values of ET0, which indicates that ClimaSG may be used as the basis for calculating crop water requirements and for irrigation design and planning purposes. The suitability of synthetic weather series generated with ClimaSG for crop modelling applications was further assessed by running the model OliveCan for low density rainfed (LD) and super high density irrigated (SHD) olive orchards for several locations in Spain. Yield distributions simulated with synthetic weather rarely differed significantly from those obtained with actual weather data, although the coefficient of variation (CV) was usually underestimated. Considering its low input requirements and satisfactory performance, ClimaSG represents a promising source of weather data for different practical applications.

Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City

The foundation of any smart city requires an innovative and robust communication infrastructure. Many research communities envision free-space optical communication (FSO) as a promising backbone technology for the services and applications provided by such cities. However, the channel through which the FSO signal travels is the atmosphere. Therefore, the FSO performance is limited by the local weather conditions. The variation in meteorological variables leads to variations of the refractive index along the transmission path. These index inhomogeneities (i.e., atmospheric turbulence) can significantly degrade the performance of FSO systems. Thus, a practical implementation of the FSO link must carefully consider the atmospheric turbulence effect. This paper aims to investigate the feasibility of FSO communication for NEOM, a promising smart city in Saudi Arabia. We study the effect of weather conditions on FSO links using the micrometeorology model, taking into account actual weather data. The FSO performance in winter and summer was compared in terms of the bit error rate, signal-to-noise ratio (SNR), link availability, and transmission distance. The study shows that the atmospheric turbulence strength is moderate and strong in winter and summer, respectively. The temperature has the biggest impact on the FSO system when compared to the other meteorological elements included in this study. Furthermore, at transmission distances less than 300 m, atmospheric turbulence does not significantly affect the FSO for the operating wavelength of 1550 nm. Furthermore, it has been shown that at transmission distances greater than 300 m, the SNR in summer is more than 18% higher than in winter. The findings of this research enable understanding of the effect of turbulence caused by NEOM weather on the FSO link, thus assisting engineers in establishing a reliable FSO backbone link by adjusting the relevant parameters.

Resiliency-Oriented Optimal Planning for a Grid-Connected System With Renewable Resources and Battery Energy Storage

This article develops practical resiliency-oriented optimal planning models for a grid-connected system with renewable resources and battery energy storage. A long-term (ten-year) operation is conducted by incorporating grid outages, operating reserve, detailed models of electricity prices, and components’ capacity degradations and costs. The number of grid outages per year as well as the days, starting time, and duration of the outages are adopted using probability distribution functions. Interest and escalation rates are updated in each year of operation to update the retail price, feed-in-tariff, and penalty rate of the electricity provider for the unsupplied power during grid outages. A novel operation strategy is developed for islanded and grid-connected modes of operation. The renewable-battery system is optimized for two cases: 1) a resilient system without load interruption; and 2) a resiliency-constrained system with partial load interruption. A case study in South Australia is conducted by incorporating the load profile of an educational campus and the actual weather data for a project lifetime of ten years. The resiliency-oriented optimal planning model is compared to the same system optimized by short-term data. It is found that the resiliency-constrained system achieves lower cost, battery capacity, and dumped energy with limited unsupplied energy compared to the resilient system, which has zero unsupplied energy. The proposed long-term resilient system is found to need 2.2-MWh battery capacity to efficiently supply 17.58-MWh electricity during the grid outages. The cost of electricity for the proposed resilient system is 23.77 ¢/kWh.

Farmers’ Perception and Adaptation Strategies to Climate Change in Central Mali

AbstractAdaptation of the agricultural sector to climate change is crucial to avoid food insecurity in sub-Saharan Africa. Farmers’ perception of climate change is a crucial element in adaptation process. The aim of this study was (i) to compare farmers’ perception of climate change with actual weather data recorded in central Mali, (ii) to identify changes in agricultural practices implemented by farmers to adapt to climate change, and (iii) to investigate the link between farmers’ perception of climate change and implementation of adaptation practices. Focus group discussions and individual surveys were conducted to identify climate-related changes perceived by farmers and agricultural adaptation strategies they consider relevant to cope with these changes. A majority (>50%) of farmers perceived an increase in temperature, decrease in rainfall, shortening of growing season, early cessation of rainfall, and increase in the frequency of dry spells at the beginning of the growing season. In line with farmers’ perception, analysis of climate data indicated (i) an increase in mean annual temperature and minimum growing season temperature and (ii) a decrease in total rainfall. Farmers’ perception of early cessation of rainfall and more-frequent drought periods were not detected by climate data analysis. To cope with the decrease in rainfall and late start of the growing season, farmers used drought-tolerant cultivars and implemented water-saving technologies. Despite a perceived warming, no specific adaptation to heat stress was mentioned by farmers. We found evidence of a link between farmers’ perception of climate change and the implementation of some adaptation options. Our study highlights the need for a dialogue between farmers and researchers to develop new strategies to compensate for the expected negative impacts of heat stress on agricultural productivity.

Hygrothermal modelling of historic rubble masonry walls: Accounting for geometric and compositional variability

An established issue when assessing the hygrothermal performance of heritage buildings is the correlation between measured data and model outputs. This can be attributed to many factors including climate data and hygrothermal material property data. Two overlooked factors are the variation in geometry and level of decay in the structure. This can be true for several historic wall assemblies with irregular geometries and elevated levels of decay. There is a lack of guidance on how and when to incorporate this into a hygrothermal model. The goal of this paper is to examine how this variability in geometry and composition may affect model outcomes using a simulation approach with stochastic methods for a sample rubble core masonry wall.Geometrically stochastic models were generated from a script accounting for varying levels of air voids in the wall and irregular stone patterns. The presence of voids alters heat, capillary and vapour transport. This was combined with hygrothermally dissimilar mortar and stone units. Simulations were performed under four boundary conditions: heat, drying, wetting and actual weather data. The results showed that geometric stochasticity can lead to a large variation of results. The presence of voids in the wall also led to a reduction in heat and moisture transport in the assemblies. The results of the stochastic simulations were then compared to a set of simplified one and two-dimensional models to judge their appropriateness. It was found that 2-D parallel like models that account for the mortar joint in some fashion are most appropriate.

Ground Risk Assessment of UAV Operations Based on Horizontal Distance Estimation under Uncertain Conditions

In this paper a ground safety assessment model is introduced based on the probability estimation of possible impact positions when unmanned aerial vehicle (UAV) crashes on the ground. By incorporating the random uncertainties during the descending process, risks associated with UAV’s ground crash are estimated accurately. The number of victims on the ground per flight hour is selected as the indicative index to evaluate the risk levels of the corresponding ground area. We mainly focus on the analysis of uncertainties that usually appear in drag coefficient which would generate a great amount of effects on the travelled horizontal distance from the failure point to the impact point on the ground, which further influences the possible impact positions. The drag force in the air, failure velocity of a UAV, and wind effects in the local area are all considered in the proposed model, as well as ground features, including sheltering effects on the ground, UAV parameter settings, and distribution of local population. Uncertainties in drag force when a UAV descends, UAV’s initial horizontal and vertical speeds at failure point, and local wind patterns are all considered as the indispensable factors in the proposed model. Especially the probability of fatality once hit by the UAV’s debris is explored to make the safety assessment more reliable and valuable. In the end, the actual UAV parameters and official historical weather data are used to estimate the risks in a real operation environment when a failure event happens at a legal flying height. Experimental results are given based on different types of UAVs and random effects in the descent. The results show that all the operations of all kinds of UAVs selected in the validation are so dangerous that the safety of people on the ground cannot be guaranteed, whose value is much bigger than the manned aircraft safety criterion 10−7.

Performance of lightweight and heavyweight building walls with naturally ventilated passive and active facades

Well-designed ventilated air-spaces behind external claddings in the building envelope can potentially reduce thermal energy flow in the entire wall structure. In this study, the impact of the dynamic thermo-hydraulic behavior of the flow in the naturally ventilated cavity on the performance of lightweight and heavyweight wall assemblies with traditional passive and active (i.e., BIPV) facades is investigated. A numerical transient 2-D model validated against experimental measurements is employed to perform the analysis using actual weather data and building practices in Europe, particularly in Switzerland. The results reveal that the change in the external cladding type from the passive fiber cement to the active PV façade can increase the time lag of the wall structure up to 2 h in summer and decrease it up to 1 h in winter. The maximum difference between the amplitudes of the heat flux through the interior surface for a wall assembly with the lightweight wall core is 1.5 times higher compared to a heavyweight wall core. The results show that enlarging the cavity thickness behind external claddings from 45 mm to 110 mm can increase the heat flow through the cavity up to 1.5 times. It is also shown that reflective insulation on the cavity surface adjacent to the wall core could increase the cladding surface temperature by more than 30% compared to the case without reflective insulation. This research shows that replacing the passive cladding with an active façade could affect the performance of the entire wall assembly, which highlights the importance of analyzing the active ventilated wall structures to transition towards modern building skins.

On-farm data-rich analysis explains yield and quantifies yield gaps of winter wheat in the U.S. central Great Plains

With an annual production of ∼60 Mt, the U.S. accounts for about 8% of the global wheat (Triticum aestivum L.) production. Still, quantification of the yield gaps (YG) and major management factors to reduce it are scarce. We used Kansas, the largest wheat producing state in the U.S. located in the central Great Plains, for an initial assessment of on-farm yield and YG. We collected field-level management (37 variables), weather (8 variables), soil (two variables) and yield data from 656 commercial wheat fields over three harvest years (2016–2018) to (i) quantify management adoption levels, Ya, and YG, and (ii) identify interactions among management practices and weather variables using a data-rich approach. We also used our data as a case-study to detect whether differences in crop management among regions justified data clustering by crop zones. Water-limited yield potential (Yw) was simulated for each field-year using actual soil and weather data and the SSM-Wheat model. Fields were grouped in three climate zones based on their long-term climatology and important differences in cropping systems between zones. Grain yield averaged 3.8 Mg ha−1 and ranged from 0.3–7.1 Mg ha−1 across all regions and years. The YG averaged 44 %, with seasons with high Yw resulting in greater YG. Management practices most often associated with grain yield were management of nitrogen (N), phosphorus (P), and sulphur (S) fertilizer, as well as foliar fungicide and its interaction with variety reaction to major diseases, although these depended on in-season weather. Our analyses highlighted many other genotype × management × environment interactions explaining winter wheat Ya, such as regional-specific cultivar maturity and the dependency of sowing date (and its relation to seeding rate) on cropping system.

Heat loss coefficients computed for floating PV modules

AbstractFor floating PV (FPV), the operating temperature of the PV modules has been a major source of uncertainty. As the operating temperature of PV modules affects their efficiency, knowledge of this parameter is critical in order to perform accurate energy yield assessment (EYA). This uncertainty is reflected in the scientific literature but has also hampered the bankability and realization of commercial FPV projects. Our work proposes a model that computes both the efficiency of heat loss to the environment for a given FPV technology and the operational cell temperature, needed to compute the module efficiency. The suggested model is applicable to different FPV technologies. We show that PV modules that are not in direct thermal contact with water have similar heat loss behavior as land‐based PV. We thereafter investigate a specific technology in which the modules are mounted on a membrane resting directly on the water body. The model is validated against actual production and weather data from deployed FPV systems. Our results show that the water temperature impacts both FPV technologies, however to a smaller degree for the air‐cooled system, where the wind is of greater influence. When in direct thermal contact with water, the water body provides superior cooling, and the resulting U‐value of about 86.5 W/m2K is significantly larger than typical values reported for land‐based modules.

Quantifying the impact of residential space heating electrification on the Texas electric grid

In this technical analysis, we studied the effects of complete electrification of space heating in the Texas residential sector on the energy consumption, peak power demand, and grid capacity utilization in the Electric Reliability Council of Texas (ERCOT) electricity grid. We utilized the National Renewable Energy Laboratory’s (NREL) ResStock tool to develop geographically representative housing stock models and the physics-based EnergyPlus modeling software to create an aggregate building stock energy model that represents the residential sector in the ERCOT operating region. In this aggregate building energy model, we replace all natural gas and other fossil-fuel furnaces with reversible electric heat pumps of varying efficiencies that can provide heating in the winter and cooling in the summer. We integrate spatially-resolved actual meteorological weather data with the building stock energy model to simulate a specific year (2016) of hourly-resolved energy usage in the ERCOT region. We find the annual electricity consumption, peak hourly power demand for each day, and load duration curves for each of 17 regions within ERCOT. From the base case, the absolute winter peak electrical power demand in the residential sector could increase by as much as 36%, or 12 GW. These results indicate that grid capacity would need to increase by 10 GW (a 25% increase for the residential sector) to accommodate a winter peaking residential sector. Though winter electricity consumption would increase for home heating, the annual amount of electricity consumption would stay roughly the same or decrease because the higher efficiency heat pumps provide more efficient cooling than the conventional air conditioners they also replace. Using average 2018 emissions rates, we estimate a change to standard efficiency heat pumps would lead to a 4.1% reduction of CO2 emissions and a 5.8% reduction of NOx emissions from the residential sector. There is no significant change in SOx emissions in our standard efficiency scenario, but in the high and ultra-high aggregate efficiency scenarios, SOx emissions are reduced by 8.3% and 15.0% respectively.

Evaluating the applicability of Typical Meteorological Year under different building designs and climate conditions

Typical year weather files are synthetic files generated from historical weather data used in building energy simulation to evaluate energy performance of buildings. The current work is to illustrate the impact of building types and designs on the deviations between the energy demand predicted using typical year weather files and the average energy demand predicted using 30-year long-term actual year weather data. This study explores design parameters, which have great impact on energy demands, including window-to-wall ratio, window solar heat gain coefficient, floor construction, and solar reflectance of exterior walls and roof. These design parameters are applied to two different building types — small and large office buildings, and two different climatic locations — Montreal and Vancouver, Canada. The results indicate that certain designs exhibit wider deviations between energy demands predicted with a typical year and the long-term average. The maximum deviation is found to be around 4.5%, which suggests that typical year weather file is still a reliable means to predict the long-term average cooling and heating energy performance. However, the study also found that typical year weather files underestimate the peak load in the long run for up to 85% of the time.

Sequence-to-sequence deep learning model for building energy consumption prediction with dynamic simulation modeling

This study used a more detailed dataset approach to address the limitations of existing building energy prediction methods and to predict building energy demand more accurately. The EnergyPlus dynamic simulation program closely modeled building envelope performance, zone division, and heating, ventilation, and air conditioning systems. Building energy simulation used actual weather data and generated occupancy data. The occupancy, lighting, and equipment schedules for each zone were generated in 5-min intervals using the Lawrence Berkeley National Laboratory occupancy simulator. Summer electric power consumption based on Internet of Things information from the testbed building validated the model. When applying generated occupancy, lighting, and equipment schedules, the mean bias error of simulation similarly improved to 4.73%, and the coefficient of variation of the root-mean-squared error (Cv(RMSE]) improved to 12.26%. Subsequently, a demand prediction model was constructed as a sequence-to-sequence (seq2seq) model using long short-term memory (LSTM) cells in recurrent neural network (RNN) algorithms, then its accuracy was evaluated. In the seq2seq model, the learning performance based on the EnergyPlus data exhibited an RMSE of 4.48% and a weighted average percentage error of 3.07%. As a result of applying prediction methods while changing climate scenarios, prediction performance also satisfied statistically significant levels. The occupancy information and solar radiation were determined to exert the greatest influence on the building energy demand prediction. • The occupant schedule for each zone was generated in 5-min intervals using the LBNL occupancy simulator. • When applying the generated internal schedules, the MBE improved to 4.73%, and the Cv(RMSE) improved to 12.26%. • In the seq2seq model, the learning performance based on the EnergyPlus data exhibited an RMSE of 4.48% and a WAPE of 3.07%. • The occupant information and solar radiation were determined to exert the greatest influence on energy demand prediction.

Occupancy data at different spatial resolutions: Building energy performance and model calibration

Occupancy is a significant area of interest within the field of building performance simulation. Through Bayesian calibration, the present study investigates the impact of the availability of different spatial resolution of occupancy data on the gap between predicted and measured energy use in buildings. The study also examines the effect of occupancy data on the quality of the constructed prediction intervals (PIs) using the Coverage Width-based Criterion (CWC) metric. CWC evaluates the PIs based on both their coverage (correctness) and width (informativeness). This investigation takes the form of an actual building case study, with nine months of hourly measured building electricity use, WiFi connection counts as a proxy for occupancy, and actual weather data. In general, the building energy model’s accuracy improves with the occupancy and plug-loads schedule derived from WiFi data. Specifically, the Coefficient of Variation Root Mean Square Error (CV[RMSE]) reduced from 37% to 24% with an exponential improvement in the PIs quality compared to the results obtained with ASHRAE 90.1 reference schedules. However, the increase in prediction accuracy shrank to 5% CV(RMSE) and a comparable CWC upon calibrating the base loads of the reference schedules. Increasing the spatial resolution from building aggregated to floor aggregated occupancy data worsened the CV(RMSE) and CWC, suggesting trade-offs between parameter uncertainty and model bias/inadequacy. These results contribute to our understanding of the interactions between model complexity, simulation objectives, and data informativeness, facilitating future discussions on the right level of abstraction when modeling occupancy.

Indoor Climate and Energy Model Calibration with Monitored Data of a Naturally Ventilated Dairy Barn in a Cold Climate

HighlightsIndoor climate and energy model of a dairy barn is constructed and calibrated with collected data.Long-term monitoring of indoor conditions and electricity consumption greatly facilitates the model calibration process.Statistical benchmarks given by guidelines confirm the usability and reliability of the model.Abstract. This study demonstrates an application of ICE model calibration by using sensor building metrics in a naturally ventilated dairy house in a cold climate. The barn, at the time of the study, had 70 lactating cows and 30 calves with a total animal area of 1922 m2 and other auxiliary areas of 268 m2. Indoor condition data were collected by four integrated sensors inside the barn for six months, from March to August 2019. IDA ICE 4.8 SP1 simulation software was used to build and simulate the model, with calibration steps conducted first manually, then statistically. Actual weather and indoor condition data during the monitored period were used for calibration; statistical indices of the calibrated model were confirmed by the benchmarks given from ASHRAE Guideline 14-2014, IPMVP version 2016, and FEMP version 4.0 2015. The yielded result was a baseline ICE model, which can be further utilized in the study of energy conservation measures (ECMs), retrofitting feasibility, and ammonia and other contaminant gas emission mitigation. The abovementioned calibration practice and the proposals built on it open a pathway to achieve a higher level of energy efficiency for this type of livestock building. Keywords: Cold weather, Dairy farms, Model calibration, Natural ventilation.

Prediction of overhead transmission line ampacity based on extreme learning machine

Caused by the fluctuation of weather conditions, the transmission line ampacity varies with time. For power system operators, the accurate forecast results of overhead transmission lines ampacity are very important and helpful in making planning and control decisions. To assist the system operators in making better use of the transfer capability of transmission lines, it is urgent to improve the ampacity forecast results. In this paper, a novel method based on the extreme learning machine (ELM) is proposed to predict the ampacity. Taking the historical weather and ampacity data as the input data, an ELM-based method can predict the ampacity rapidly and accurately. Numerical simulations based on the recorded actual weather data around a transmission line validate the efficiency of the ELM-based ampacity forecast method.

The Influence of Weather Conditions on the Optimal Setting of Photovoltaic Thermal Hybrid Solar Collectors—A Case Study

The potential absorption of solar energy in photovoltaic thermal (PVT) hybrid solar collectors at different tilt angles was compared in the present study. The optimal tilt angles were tested in three variants: during 1 day, 1 year and a period of 30 years. Simulations were performed based on actual weather data for 30 years, including average hourly total radiation, insolation and air temperature. The apparent movement of the Sun across the sky, solar radiation properties, and the electrical and thermal efficiency of a PVT collector were also taken into account in the simulation model. The optimal orientation of the absorber surface was determined by solving an optimization task. The results of the study indicate that in the long-term perspective, the collector’s performance is maximized when the absorber is positioned toward the south at an elevation angle of 34.1°.

A systematic approach in constructing typical meteorological year weather files using machine learning

A typical meteorological year weather file, supposedly representing the climatic conditions, is commonly used as an input for building energy simulation to predict the long-term performance of the buildings. Depending on the building type and climatic locations, the deviation between the actual long-term average energy performance and the predicted one may not be insignificant. Weather files are synthetically constructed on historical weather data over a long period of time for an array of weather parameters, such as solar radiation, temperature, wind speed and others. The statistical procedure to construct the weather files depends on the weights assigned to these weather parameters. Under current practice, these weighting factors are universally assigned regardless of climatic locations nor building applications based on experts’ judgment. The objective of this paper is to propose a novel approach in assigning weighting factors systematically, demonstrate its applicability, and quantify the potential reduction in deviation.Instead of basing on experts’ judgement, Random Forest algorithm was deployed to extract the feature importance of the weather parameters in order to assign weighting factors straightly proportional to their impacts on energy performance of buildings. To ensure the assigned weighting factorsfully reflect the climatic characteristics, five different training approaches were proposed, and the resulting climatic location dependent weather files were constructed and cross-validated.The newly constructed typical meteorological year weather files were applied to two different climatic locations to investigate the representativeness of these new weather files as compared to existing weather files and historical weather data of actual years. The representativeness was indicated in terms of the deviation in predicted energy performance of buildings between using the typical meteorological year weather file and actual historical weather data. The results indicated that typical meteorological year weather file based on the novel approach offers better prediction (with statistical significance) on energy performance for climatic locations with wider temperature range. As a result, the suggested approach avoids potential under/oversizing of equipment and promotes energy conservation.

Characterization of the acceptable daylight quality in typical residential buildings in Hong Kong

Characterizing daylight quality for flats in residential buildings is critically important in Hong Kong, where buildings are close to each other. In this study, we performed Climate Based Daylight Modeling (CBDM) calculations on 400 flats in a public housing property using three different methods. In addition to the Typical Meteorological Year (TMY) data, the actual weather data was measured throughout an entire year and used in the CBDM calculations. At the same time, a questionnaire survey was conducted to collect the residents' subjective evaluations on the daylight quality and luminous environment in their flats from a long-term perspective. Based on the 340 completed surveys, it was found that the residents' satisfaction with daylighting had the most significant influence on their satisfaction with overall luminous environment. Two measures—the average sDA300/50% and the maximum average daylight illuminance—were found to be highly correlated to the residents’ satisfaction with daylight in their flats. A flat with an average sDA300/50% above 66% and a maximum average daylight illuminance above 5,624 lx was found to provide acceptable daylight quality for typical residential buildings in Hong Kong.