Indoor Temperature Validation of Low-Income Detached Dwellings under Tropical Weather Conditions

Urban territorial expansion generated in the last decades has brought a series of consequences, such as the variation between urban and suburban weather conditions affecting indoor temperature and increasing electricity consumption derived from the use of cooling systems. Current approaches of simulation models in residential buildings use indoor environmental data for carrying out validations to propose hygrothermal comfort alternatives for the mitigation of the effects of the external environmental conditions on the interior spaces of dwellings. In this work, an hourly evaluation of both indoor and outdoor environmental parameters of two case studies in a tropical climate was carried out, by means of a whole-building simulation approach tool during a week representative of the warmest period of the year. The integration of the collected environmental data in the theoretical model allowed us to reduce the error range of the estimated indoor temperature with results in normalized mean bias error between 7.10% and −0.74% and in coefficient of variation of the root mean square error between 16.72% and 2.62%, in the different indoor zones of the case studies. At the same time, the energy assessment showed a difference of 33% in Case 1 and −217% in Case 2 for final electricity consumption.

Download Full-text

Energy models for cost-optimal analysis: Development and calibration of residential reference building models for Northern Cyprus

Indoor and Built Environment ◽

10.1177/1420326x211013076 ◽

2021 ◽

pp. 1420326X2110130

Author(s):

Manta Marcelinus Dakyen ◽

Mustafa Dagbasi ◽

Murat Özdenefe

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Mean Squared Error ◽

Residential Buildings ◽

Mean Bias Error ◽

Bias Error ◽

Sampled Data ◽

Low Carbon ◽

Building Stock ◽

Northern Cyprus

Ambitious energy efficiency goals constitute an important roadmap towards attaining a low-carbon society. Thus, various building-related stakeholders have introduced regulations targeting the energy efficiency of buildings. However, some countries still lack such policies. This paper is an effort to help bridge this gap for Northern Cyprus, a country devoid of building energy regulations that still experiences electrical energy production and distribution challenges, principally by establishing reference residential buildings which can be the cornerstone for prospective building regulations. Statistical analysis of available building stock data was performed to determine existing residential reference buildings. Five residential reference buildings with distinct configurations that constituted over 75% floor area share of the sampled data emerged, with floor areas varying from 191 to 1006 m2. EnergyPlus models were developed and calibrated for five residential reference buildings against yearly measured electricity consumption. Values of Mean Bias Error (MBE) and Cumulative Variation of Root Mean Squared Error CV(RMSE) between the models’ energy consumption and real energy consumption on monthly based analysis varied within the following ranges: (MBE)monthly from –0.12% to 2.01% and CV(RMSE)monthly from 1.35% to 2.96%. Thermal energy required to maintain the models' setpoint temperatures for cooling and heating varied from 6,134 to 11,451 kWh/year.

Download Full-text

Energy saving potential of tubular light pipe system with different colors on internal surfaces

International Journal of Energy Sector Management ◽

10.1108/ijesm-12-2018-0001 ◽

2019 ◽

Vol 14 (4) ◽

pp. 793-837

Author(s):

Abdus Salam Azad ◽

Mohd Salman ◽

S.C. Kaushik ◽

Dibakar Rakshit

Keyword(s):

Prediction Models ◽

Residential Buildings ◽

Mean Bias Error ◽

Bias Error ◽

New Delhi ◽

Content Type ◽

Pipe System ◽

Solar Altitude ◽

Light Pipe ◽

Different Seasons

Purpose Lighting in building sectors (consumes the highest energy in commercial buildings and the second highest in residential buildings in India) has very much potential for energy conservation in buildings. With the use of daylighting system, energy consumption in lighting can be lowered up to 30 to 40 per cent. Design/methodology/approach An experimental effort has been made in this paper to explore the internal wall coloring effect on the performance of tubular light pipe. Trace-pro software has been used and validated. With the help of this software, light pipe has been designed and simulated in a ray tracing mode. Assessment of four globally used prediction models has also been conducted to compare the performances in different seasons for light pipes in the composite climate of New Delhi. Findings It has been conducted based on three statistical indicators as mean bias error, root mean square error and R2. Using regression, an empirical model for average internal illuminance has been developed as a function of light reflectance value (LRV) and solar altitude angle. Trace-pro results confirmed that maximum internal illuminance can be obtained with wall surfaces coated with high LRV color. Finally, by using of a single light pipe system for a test room with the artificial lighting system and applying continuous dimming control, the amount of electrical energy has been saved up to 38.5 per cent per year. Originality/value After going through the literature, it has been identified that there has been no paper published which explores the effect of colors of the internal walls on the performance of the light pipe. Along with this, the comparison between existing empirical performance models and find out which model gives the best result in different seasons has been carried out for New Delhi, India.

Download Full-text

Classification of Failures in Photovoltaic Systems using Data Mining Techniques

Big Data ◽

10.4018/978-1-4666-9840-6.ch062 ◽

2016 ◽

pp. 1347-1366

Author(s):

Lucía Serrano-Luján ◽

Jose Manuel Cadenas ◽

Antonio Urbina

Keyword(s):

Data Mining ◽

Environmental Parameters ◽

Weather Conditions ◽

Environmental Data ◽

Photovoltaic System ◽

Data Mining Techniques ◽

Learning Techniques ◽

And Performance ◽

Using Data ◽

Bad Weather

Data mining techniques have been used on data collected from a photovoltaic system to predict its generation and performance. Nevertheless, up to date, this computing approach has needed the simultaneous measurement of environmental parameters that are collected by an array of sensors. This chapter presents the application of several computing learning techniques to electrical data in order to detect and classify the occurrence of failures (i.e. shadows, bad weather conditions, etc.) without using environmental data. The results of a 222kWp (CdTe) case study show how the application of computing learning algorithms can be used to improve the management and performance of photovoltaic generators without relying on environmental parameters.

Download Full-text

Classification of Failures in Photovoltaic Systems using Data Mining Techniques

Advances in Environmental Engineering and Green Technologies - Soft Computing Applications for Renewable Energy and Energy Efficiency ◽

10.4018/978-1-4666-6631-3.ch012 ◽

2015 ◽

pp. 300-319 ◽

Cited By ~ 1

Author(s):

Lucía Serrano-Luján ◽

Jose Manuel Cadenas ◽

Antonio Urbina

Keyword(s):

Data Mining ◽

Environmental Parameters ◽

Weather Conditions ◽

Environmental Data ◽

Photovoltaic System ◽

Data Mining Techniques ◽

Learning Techniques ◽

And Performance ◽

Using Data ◽

Bad Weather

Download Full-text

Power Prediction and Technoeconomic Analysis of a Solar PV Power Plant by MLP-ABC and COMFAR III, considering Cloudy Weather Conditions

International Journal of Chemical Engineering ◽

10.1155/2016/1031943 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

M. Khademi ◽

M. Moadel ◽

A. Khosravi

Keyword(s):

Power Plant ◽

Output Power ◽

Weather Conditions ◽

Mean Bias Error ◽

Percentage Error ◽

Bias Error ◽

Solar Pv ◽

Economic Point ◽

Cloudy Weather ◽

The Mean

The prediction of power generated by photovoltaic (PV) panels in different climates is of great importance. The aim of this paper is to predict the output power of a 3.2 kW PV power plant using the MLP-ABC (multilayer perceptron-artificial bee colony) algorithm. Experimental data (ambient temperature, solar radiation, and relative humidity) was gathered at five-minute intervals from Tehran University’s PV Power Plant from September 22nd, 2012, to January 14th, 2013. Following data validation, 10665 data sets, equivalent to 35 days, were used in the analysis. The output power was predicted using the MLP-ABC algorithm with the mean absolute percentage error (MAPE), the mean bias error (MBE), and correlation coefficient (R2), of 3.7, 3.1, and 94.7%, respectively. The optimized configuration of the network consisted of two hidden layers. The first layer had four neurons and the second had two neurons. A detailed economic analysis is also presented for sunny and cloudy weather conditions using COMFAR III software. A detailed cost analysis indicated that the total investment’s payback period would be 3.83 years in sunny periods and 4.08 years in cloudy periods. The results showed that the solar PV power plant is feasible from an economic point of view in both cloudy and sunny weather conditions.

Download Full-text

Numerical Investigation of Phase Change Materials for Insulation of Residential Buildings in Hot Regions in Iraqi cities

Wasit Journal of Engineering Sciences ◽

10.31185/ejuow.vol6.iss2.87 ◽

2018 ◽

Vol 6 (2) ◽

pp. 1-20

Author(s):

Hadi O. Basher ◽

Mushtaq I. Hasan ◽

Ahmed O. Shdhan

Keyword(s):

Phase Change Materials ◽

Numerical Study ◽

Residential Buildings ◽

Electricity Consumption ◽

R Software ◽

Indoor Temperature ◽

Insulation Materials ◽

Energy Plus ◽

South Wall ◽

North Wall

In this paper, numerical study has been conducted for using PCM as thermal insulation materials by incorporating it with layers of walls and ceiling of buildings. The effect of PCM and its role in improvement of thermal performance and thermal comfort is numerically studied. ESP-r software program has been used for numerical simulation in this paper. Energy plus weather database software was used to create climate date for Kut city (32.5 oN 45.8 oE) that used for simulation in this study. Two identical rooms were inserted in software ESP-r with dimensions (1.5m*1.5m*1m), the first is standard room for comparison and the second is test room for experimenting. Many cases were studied according to the thickness of the PCM and according to the orientation (North wall, South wall, East wall, West wall, and ceiling). Results obtained showed a reduction in indoor temperature of the zone and the reduction in the cooling load and as a result saving in electricity consumption with using PCM as insulation materials.

Download Full-text

Estimating Solar Insolation and Power Generation of Photovoltaic Systems Using Previous Day Weather Data

Advances in Civil Engineering ◽

10.1155/2020/8701368 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Min Hee Chung

Keyword(s):

Energy Production ◽

Mean Squared Error ◽

Weather Conditions ◽

Absolute Error ◽

Weather Data ◽

Mean Bias Error ◽

Bias Error ◽

Feed Forward Neural Network ◽

Pv System ◽

Solar Insolation

Day-ahead predictions of solar insolation are useful for forecasting the energy production of photovoltaic (PV) systems attached to buildings, and accurate forecasts are essential for operational efficiency and trading markets. In this study, a multilayer feed-forward neural network-based model that predicts the next day’s solar insolation by taking into consideration the weather conditions of the present day was proposed. The proposed insolation model was employed to estimate the energy production of a real PV system located in South Korea. Validation research was performed by comparing the model’s estimated energy production with the measured energy production data collected during the PV system operation. The accuracy indices for the optimal model, which included the root mean squared error, mean bias error, and mean absolute error, were 1.43 kWh/m2/day, −0.09 kWh/m2/day, and 1.15 kWh/m2/day, respectively. These values indicate that the proposed model is capable of producing reasonable insolation predictions; however, additional work is needed to achieve accurate estimates for energy trading.

Download Full-text

The Living Community Challenge for Environmental and Urban Sustainability

UF Journal of Undergraduate Research ◽

10.32473/ufjur.v22i0.121611 ◽

2020 ◽

Vol 22 ◽

Author(s):

Larissa Krinos

Keyword(s):

Case Studies ◽

Low Income ◽

Urban Sustainability ◽

Certification Program ◽

Certification Programs ◽

Living Building Challenge ◽

Green Certification

The Living Community Challenge (LCC) is a green certification program that, unlike most certification programs, is geared toward whole neighborhoods as opposed to singular buildings. Unfortunately, no existing communities have achieved Living Community Challenge certification. Still, there are many neighborhoods utilizing the ideals – known as petals – of the LCC in attempts to become more sustainable. The Living Building Challenge (LBC), the parent certification for the LCC, has seen more success than the LCC and will provide further research on the implications of its criterion. This paper will look at the hypothetical variables of the LCC, the communities trying to achieve these variables, and how elements of it could be used in relation to impoverished communities. Through case studies on groups and individuals attempting LCC and LBC certification, specifically Bend, Oregon and the BLOCK Project, the potential of the research becomes evident. This paper seeks to demonstrate how the LCC could be applied specifically in low-income areas in Gainesville, FL without achieving all the requirements of each petal.

Download Full-text

Correction of Eddy Covariance Based Crop ET Considering the Heat Flux Source Area

Atmosphere ◽

10.3390/atmos12020281 ◽

2021 ◽

Vol 12 (2) ◽

pp. 281

Author(s):

Stuart L. Joy ◽

José L. Chávez

Keyword(s):

Heat Flux ◽

Eddy Covariance ◽

Agricultural Land ◽

Flux Measurement ◽

Source Area ◽

Mean Bias Error ◽

Bias Error ◽

Crop Water ◽

The Past ◽

Measurement Results

Eddy covariance (EC) systems are being used to measure sensible heat (H) and latent heat (LE) fluxes in order to determine crop water use or evapotranspiration (ET). The reliability of EC measurements depends on meeting certain meteorological assumptions; the most important of such are horizontal homogeneity, stationarity, and non-advective conditions. Over heterogeneous surfaces, the spatial context of the measurement must be known in order to properly interpret the magnitude of the heat flux measurement results. Over the past decades, there has been a proliferation of ‘heat flux source area’ (i.e., footprint) modeling studies, but only a few have explored the accuracy of the models over heterogeneous agricultural land. A composite ET estimate was created by using the estimated footprint weights for an EC system in the upwind corner of four fields and separate ET estimates from each of these fields. Three analytical footprint models were evaluated by comparing the composite ET to the measured ET. All three models performed consistently well, with an average mean bias error (MBE) of about −0.03 mm h−1 (−4.4%) and root mean square error (RMSE) of 0.09 mm h−1 (10.9%). The same three footprint models were then used to adjust the EC-measured ET to account for the fraction of the footprint that extended beyond the field of interest. The effectiveness of the footprint adjustment was determined by comparing the adjusted ET estimates with the lysimetric ET measurements from within the same field. This correction decreased the absolute hourly ET MBE by 8%, and the RMSE by 1%.

Download Full-text

CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

Remote Sensing ◽

10.3390/rs13152996 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2996

Author(s):

Qinwei Zhang ◽

Mingqi Li ◽

Maohua Wang ◽

Arthur Paul Mizzi ◽

Yongjian Huang ◽

...

Keyword(s):

United States ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Co2 Flux ◽

Total Carbon ◽

Mean Bias Error ◽

Bias Error ◽

Parallel Experiment ◽

Concentration Measurements ◽

Global Stocktake

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

Download Full-text