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

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Thermal sensitivity and adaptive comfort in mixed-mode office buildings in humid subtropical climate

This study evaluates thermal sensitivity, expectations, and adaptability in mixed-mode ventilated buildings by subjective and objective assessments. A field study of simultaneous measurements of physical environmental parameters and right-here-right-now surveys was conducted in three office buildings from summer to winter in humid subtropical climates. The study confirmed previous findings that thermal adaptability and preferences changes as the ventilation system switches between natural ventilation (NV) and air-conditioning (AC). Occupants in this study showed more sensitivity to temperature changes than previously reported by ASHRAE Standards, yet thermal sensitivity was the same under different modes of operation. The neutral temperature was 0.5 oC higher than the predicted neutral temperature by ASHRAE 55 under both modes of operation. Thermal expectations were 1.5 oC warmer than the neutral temperature estimated by ASHRAE 55 when the building was operated by AC, while thermal expectations were almost the same as predicted by ASHRAE 55 under NV mode. Thermal comfort range was 0.75 oC wider than those predicted by ASHRAE 55 under NV operation, and the same as ASHRAE 55 under AC operation. The findings of this research provide a better understanding of mixed-mode building operations and perspectives from occupants, important for building designers and building operation managers.</p

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

A novel machine learning-based framework for mapping outdoor thermal comfort

Rapid urbanization and global warming have increased heat stress in urban areas. This in turn makes using indoor space more compelling and leads to more energy consumption. Therefore, paying attention to outdoor spaces design with thermal comfort in mind becomes more important since outdoor spaces can host a variety of activities. This research aims to introduce a machine learning-based framework to predict the effects of different urban configurations (i.e. different greening configurations and types, different façade materials, and different urban geometry) on outdoor thermal comfort through training a pix2pix Convolutional generative adversarial network (cGAN) model. For the training of the machine learning model, a dataset consisting of 208 coupled pictures of input and output has been created. The simulation of this data has been carried out by ENVI-met. The resulting machine learning model had a Structural Similarity Index (SSIM) of 96% on the test dataset with the highest SSIM of 97.08 and lowest of 94.43 which shows the high accuracy of the model and it could have reached an answer in 3 s compared to the 30-min average time for ENVI-met simulation. The resulting model shows great promise for assisting researchers and urban designers in studying existing urban contexts or planning new developments. HIGHLIGHTS Machine learning use in outdoor thermal comfort assessment has been investigated. Vegetation, urban geometry, surface albedo, and water bodies have been studied parameters. Vegetation and street orientation have the highest and water bodies have the least impact on outdoor thermal comfort. Pix2pix algorithm implementation could create thermal comfort maps with 96% SSIM.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

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper

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

This dataset contains key characteristics about the data described in the Data Descriptor The Scales Project, a cross-national dataset on the interpretation of thermal perception scales. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata