Visual comfort assessment of daylit and sunlit areas: A longitudinal field survey in classrooms in Kashan, Iran

Visual comfort in schools enhances not only health and wellbeing, but also satisfaction and therefore learning and visual performance. This research aims at testing students’ evaluations on visual comfort through questionnaires in daylit and non-daylit areas in classrooms. Dynamic daylight metrics including Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE), codified in LEED v4, are calculated and compared to students’ evaluations. A typical high school in Kashan was selected in which subjective and field measurements were carried out simultaneously in two different oriented (south and north) classrooms during a school year (2014–2015). Simulation results show that 71% of the space in south facing classroom and 20% of the space in north facing classroom receives adequate amount of daylight while 29% of the space in south facing classroom and 0% of it in north facing classroom receives excessive amount of sunlight. According to simulations, each classroom has been divided into daylit and sunlit areas, in which students’ assessments about daylight and sunlight have been separately analyzed based on their position. Comparing simulation and survey results show that while students’ evaluation about daylight availability in daylit areas is mostly positive, daylight uniformity is not considered “enough” in these areas. Moreover, students’ impression about daylight availability in non-daylit areas is rather neutral and more optimistic than simulation results. More interestingly, most students in both sunlit and non-sunlit areas of classrooms do not feel much direct sunlight and glare. In fact, questionnaires’ results show a wider range of sunlight acceptance in south facing classroom and visual comfort in north facing classroom than simulation results. According to the results non-daylit areas or sun-lit areas defined by dynamic metrics would not necessarily cause visual discomfort, suggesting that some other factors (e.g., view, configurations of windows, expectations and region) can change the degree of comfort experienced in each space

Energy and economic performance of rooftop PV panels in the hot and dry climate of Iran

Photovoltaic (PV) Panels, one of the more promising renewable energy technologies, are growing rapidly nowadays, especially in developed countries. However, these systems have not achieved public acceptance in some countries due to low energy efficiency and poor economic performance, especially in countries which are subsidized in energy tariffs. In this paper, the energy and economic performance of fourteen rooftop PV systems with the power of 5 kW in the hot and dry climate of Iran are assessed by monitoring the total annual energy production and simulation. The monitored data is used to analyze systems’ economic performance via Pay-Back Period (PBP), Net Present Value (NPV), Return of Investment (ROI) and Levelized Cost of Energy (LCOE). Results show that single array configuration systems have the maximum energy production while dividing the system decreases the production. Economic analysis shows that the average PBP is 11.6 years under actual price of electricity (0.21$), however it is 46.9–50.5 years under subsidized average tariffs. ROI values range from 2.6 to 3.2 with the average of 2.9 for actual prices. Under subsidized prices, the cash generated by investment cannot even offset the costs that the investment requires during its lifetime with NCF and NPV being both negative. Overall, the systems are not economically beneficial under subsidized average tariffs in Iran, which discourages private and public sectors to investment on these systems. Environmentally, each PV system can averagely reduce 500 kg CO2 emission in the first year of installation and fourteen of them can approximately reduce 1,613,900 kg of CO2 emission during life time of PV panels

Evaluating assumptions of scales for subjective assessment of thermal environments – Do laypersons perceive them the way, we researchers believe?

International audienc

A Review of District-scale Energy Performance Analysis: Outlooks towards Holistic Urban Frameworks

Over the past few decades, the world has experienced a major population shift towards urban areas resulting in environmental degradation and increased energy consumption. To combat these challenges, energy efficiency measures are being deployed to improve the performance of different entities within urban built environments. However, effective implementation of such measures often requires a holistic approach to account for existing interrelated and complex relationships between entities at the urban scale. This paper presents a distillation of salient facts and approaches for energy performance evaluation of districts. The studies are reviewed in three sections; (1) concepts defining district energy performance, (2) approaches and methodologies for district energy performance evaluation and (3) system interactions between district entities. The state of the art review reveals that several challenges exist in the initial stages of energy performance assessment of districts. The suggested framework in this paper addresses this issue through pre-processing of data related to entities such as transportation systems and buildings. The framework classifies the available information under three potential categories, namely, 'subject and Scope’, ‘Input Data Management’ and ‘Methods’. This categorisation results in easier integration of multidisciplinary aspects of entities involved in district energy performance assessment.Science Foundation Irelan

An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design

Purpose: In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design. Design/methodology/approach: A methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth. Findings: The results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds. Originality/value: The proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.History & Complexit

Room energy demand and thermal comfort predictions in early stages of design based on the Machine Learning methods

Recent studies have focused on data-driven methods for building energy efficiency, by using simulated or empirical data, for energy-based design assessment rather than the common physics-based techniques, which are mostly time-consuming. In this paper, the feasibility of using seven different Machine Learning models, including three single models and four ensemble ones, is studied to predict annual energy demand and thermal comfort of the model. For this purpose, 3024 synthetic samples of a single zone model with seven input features are simulated through the EnergyPlus engine for training in addition to 360 unseen samples as testing data for accuracy reporting. Heating and cooling demands, in addition to five annual thermal comfort indices, are calculated for each data point and used as target indices. Results show Extremely Randomized Trees and Random Forest models had the highest R2 of 0.99 and 0.85 for cooling and heating demands respectively. Also, the R2 of these models for predicting annual comfort was between 0.71 and 0.95. Results are then used to develop a prediction framework of thermal comfort and energy demand performance in the early stages of building design, where most of the information about building characteristics is not yet known.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Building Physic

The Scales Project, a cross-national dataset on the interpretation of thermal perception scales

Thermal discomfort is one of the main triggers for occupants’ interactions with components of the built environment such as adjustments of thermostats and/or opening windows and strongly related to the energy use in buildings. Understanding causes for thermal (dis-)comfort is crucial for design and operation of any type of building. The assessment of human thermal perception through rating scales, for example in post-occupancy studies, has been applied for several decades; however, long-existing assumptions related to these rating scales had been questioned by several researchers. The aim of this study was to gain deeper knowledge on contextual influences on the interpretation of thermal perception scales and their verbal anchors by survey participants. A questionnaire was designed and consequently applied in 21 language versions. These surveys were conducted in 57 cities in 30 countries resulting in a dataset containing responses from 8225 participants. The database offers potential for further analysis in the areas of building design and operation, psycho-physical relationships between human perception and the built environment, and linguistic analyses

Publisher Correction: The Scales Project, a cross-national dataset on the interpretation of thermal perception scales

International audienc