scholarly journals Performance simulation of semi-transparent photovoltaic glass on a skylight for commercial building

2021 ◽  
Vol 12 ◽  
pp. e021009
Author(s):  
Leno Pôrto Dutra ◽  
Isabel Tourinho Salamoni ◽  
Eduardo Grala da Cunha
Keyword(s):  
Computational Simulation ◽  
Variable Parameter ◽  
Electricity Consumption ◽  
Electric Load ◽  
Research Subject ◽  
Commercial Building ◽  
Performance Simulation ◽  
Transparent Glass ◽  
Photovoltaic Conversion ◽  
Photovoltaic Technology

The demand for energy in buildings is a worldwide research subject due to its importance in the global electric load share. Besides, photovoltaic conversion to generate electricity locally is one of the ways to meet that demand. This work aims to evaluate the application of semi-transparent photovoltaic glass on a skylight of a commercial building and estimate electricity consumption and production using computational simulation with EnergyPlus. The opening size was set as a variable parameter. Its performance was compared to an ordinary skylight and opaque modules under the same conditions for three different Brazilian bioclimatic zones. Results show that the area's change provided significant differences in generation and less important ones in consumption. Among the bioclimatic zones, the building presented the lowest consumption and the highest generation for all configurations in the coldest zone, making it the best region for net electricity, i.e., purchased from a utility. Comparing semi-transparent with opaque modules, the latter produced much more energy, but consumption was reduced by an average of 28% in favour of the photovoltaic glass. The main conclusion is that the use of photovoltaic technology in a semi-transparent glass is promising regarding the integration of generators to the building, but the efficiency rates need to increase to bring it closer to opaque modules in performance.

scholarly journals Experimental Analysis on Solid Desiccant Used in An Air Conditioning

2019 ◽  
Vol 130 ◽  
pp. 01007
Author(s):  
Ekadewi Anggraini Handoyo ◽  
Andriono Slamet ◽  
Muhammad Danang Birowosuto
Keyword(s):  
Silica Gel ◽  
Electricity Consumption ◽  
Air Cooling ◽  
Air Conditioner ◽  
Vapor Compression ◽  
Commercial Building ◽  
Vapor Compression Refrigeration Cycle ◽  
Vapor Compression Refrigeration ◽  
Closed Door ◽  
Consumption Saving

Garden by The Bay in Singapore is the world’s largest coolest conservatories. Although it is located in tropics and uses so many glasses, its electricity consumption is as much as a commercial building. The key to this low consumption is in air cooling technology. Air used for cooling the conservatories is dehumidified first using liquid desiccants before cooled. The same technology was implemented to a single-split air conditioner (AC) that works on a vapor-compression refrigeration cycle. The experiments were conducted in a room with opened and closed door. Instead of using a liquid desiccant, the experiment used a solid desiccant, i.e., silica gel which thickness was 6 mm and 8 mm with density equals to 1.27 gr cm–3. From the experiment, it is found that: (i) the thicker the silica gel, the higher outlet air temperature from silica gel, (ii) less condensate will be produced when the silica gel used is thicker, (iii) silica gel is suitable for reducing humidity of outdoor/fresh air, and (iv) the electricity consumption saving for inserting 8 mm silica gel is only 4 % when the door is closed and 31 % when the door is opened.

scholarly journals Lighting control network based on KNX protocol, for the reduction of energy consumption

2020 ◽  
Vol 19 (3) ◽  
pp. 1186
Author(s):  
Omar Chamorro Atalaya ◽  
Angel Quesquen-Porras ◽  
Dora Arce Santillan
Keyword(s):  
Energy Consumption ◽  
Network Architecture ◽  
Electricity Consumption ◽  
Environmental Benefits ◽  
Control Network ◽  
Commercial Building ◽  
Lighting Control ◽  
Reduction Of Energy Consumption ◽  
Carbon Dioxide Co2 ◽  
Air Conditioning Systems

<span>This article presents the development of a lighting control network to reduce the energy consumption of a commercial building, using the KNX protocol; because of the high rates of electricity consumption, the same that are reflected in the payment of the electricity supply. For this, the design of the network architecture is carried out, the tree type quality and it has KNX, DALI components and LED luminaires, which are interconnected by means of an Ethernet type BUS; The KNX protocol configuration is then performed using the ETS version 5 software; carries out the implementation of KNX technology, determines the reduction of energy consumption by 82.33%. Likewise, emissions of carbon dioxide (CO2), one of the main gases involved in climate change, were reduced by 85%. With these results we obtain economic and environmental benefits; Reason why it is proposed to perform the same procedure for the control of air conditioning systems, since their operation represents 32.8% of the energy consumption of an establishment.</span>

DATA SELECTION TEST METHOD FOR BETTER PREDICTION OF BUILDING ELECTRICITY CONSUMPTION

2016 ◽  
Vol 78 (5-7) ◽  
Author(s):  
Iqbal Faridian Syah ◽  
Md Pauzi Abdullah ◽  
Husna Syadli ◽  
Mohammad Yusri Hassan ◽  
Faridah Hussin
Keyword(s):  
Prediction Accuracy ◽  
Historical Data ◽  
Moving Average ◽  
Electricity Consumption ◽  
Test Method ◽  
Data Selection ◽  
Commercial Building ◽  
Data Set ◽  
Selection Test ◽  
Accuracy Of Prediction

The issue of obtaining an accurate prediction of electricity consumption has been widely discussed by many previous works. Various techniques have been used such as statistical method, time-series, heuristic methods and many more. Whatever the technique used, the accuracy of prediction depends on the availability of historical data as well as the proper selection of the data. Even the data is exhaustive; it must be selected so that the prediction accuracy can be improved. This paper presented a test method named Data Selection Test (DST) method that can be used to test the historical data to select the correct data set for prediction. The DST method is demonstrated and tested on practical electricity consumption data of a selected commercial building. Three different prediction methods are used (ie. Moving Average, MA, Exponential Smoothing, ES and Linear Regression, LR) to evaluate the prediction accuracy by using the data set recommended by the DST method.  

scholarly journals Day-Ahead Prediction of Microgrid Electricity Demand Using a Hybrid Artificial Intelligence Model

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 320 ◽  
Author(s):  
Yuan-Jia Ma ◽  
Ming-Yue Zhai
Keyword(s):  
Artificial Intelligence ◽  
Optimization Technique ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Small Error ◽  
Data Series ◽  
Time Interval ◽  
Electric Load ◽  
Demand Forecast

Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target variable (electric load data series) to obtain a cluster of enhanced-feature subseries. The extracted subseries of the past values of the electric load demand data are employed as the target variables to model the FFANN. The SA optimization technique is employed to obtain the optimal values of the FFANN weight parameters during the training process. Historical information of actual electricity consumption, meteorological variables, daily variations, weekly variations, and working/non-working day indicators have been employed to develop the forecasting tool of the devised integrated AI based approach. The approach is validated using electricity demand data of an operational microgrid in Beijing, China. The prediction results are presented for future testing days with one-hour time interval. The validation results demonstrated that the devised approach is capable to forecast the microgrid electricity demand with acceptably small error and reasonably short computation time. Moreover, the prediction performance of the devised approach has been evaluated relative to other four approaches and resulted in better prediction accuracy.

scholarly journals Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1201 ◽  
Author(s):  
Moon Kim ◽  
Jaehoon Cha ◽  
Eunmi Lee ◽  
Van Pham ◽  
Sanghyuk Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Electricity Consumption ◽  
Shopping Mall ◽  
Commercial Building ◽  
Rapid Urbanization ◽  
Data Set ◽  
Training Time ◽  
Building Model

With growing urbanization, it has become necessary to manage this growth smartly. Specifically, increased electrical energy consumption has become a rapid urbanization trend in China. A building model based on a neural network was proposed to overcome the difficulties of analytical modelling. However, increased amounts of data, repetitive computation, and training time become a limitation of this approach. A simplified model can be used instead of the full order model if the performance is acceptable. In order to select effective data, Mean Impact Value (MIV) has been applied to select meaningful data. To verify this neural network method, we used real electricity consumption data of a shopping mall in China as a case study. In this paper, a Bayesian Regularization Neural Network (BRNN) is utilized to avoid overfitting due to the small amount of data. With the simplified data set, the building model showed reasonable performance. The mean of Root Mean Square Error achieved is around 10% with respect to the actual consumption and the standard deviation is low, which reflects the model’s reliability. We also compare the results with our previous approach using the Levenberg–Marquardt back propagation (LM-BP) method. The main difference is the output reliability of the two methods. LM-BP shows higher error than BRNN due to overfitting. BRNN shows reliable prediction results when the simplified neural network model is applied.

Calibration Procedure for Energy Performance Simulation of a Commercial Building

2003 ◽  
Vol 125 (3) ◽  
pp. 251-257 ◽  
Author(s):  
Jongho Yoon ◽  
E. J. Lee ◽  
D. E. Claridge
Keyword(s):  
Building Energy ◽  
Calibration Procedure ◽  
Energy Performance ◽  
Performance Model ◽  
Commercial Building ◽  
Energy Audit ◽  
Billing Data ◽  
Performance Simulation ◽  
Base Load

Calibration of an energy simulation with actual data has generally been considered too difficult to be part of the energy audit procedure. The purpose of this paper is to develop a systematic method using a “base load analysis approach” to calibrate a building energy performance model with a combination of monthly utility billing data and sub-metered data such as is commonly available in large buildings in Korea. The calibration procedure was specifically developed to be suitable for use in both the audit and savings determination procedure within a retrofit process. The procedure has been visualized using a logical flow chart and demonstrated using the simulation of a 26-story commercial building located in Seoul as a case study. The results indicate that the approach developed provided a reliable and accurate simulation of the monthly and annual building energy requirements of the case study building.

A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model

1993 ◽  
Vol 115 (2) ◽  
pp. 77-84 ◽  
Author(s):  
D. Ruch ◽  
Lu Chen ◽  
J. S. Haberl ◽  
D. E. Claridge
Keyword(s):  
Principal Component Analysis ◽  
Change Point ◽  
Electricity Consumption ◽  
Principal Component ◽  
Component Analysis ◽  
Commercial Buildings ◽  
Grocery Store ◽  
Commercial Building ◽  
Point Model ◽  
Change Point Model

A new method for predicting daily whole-building electricity usage in a commercial building has been developed. This method utilizes a Principal Component Analysis (PCA) of intercorrelated influencing parameters (e.g., dry-bulb temperature, solar radiation and humidity) to predict electricity consumption in conjunction with a change-point model. This paper describes the PCA procedure and presents the results of its application in conjunction with a change-point regression, to predict whole-building electricity consumption for a commercial grocery store. Comparison of the results with a traditional Multiple Linear Regression (MLR) analysis indicates that a change-point, Principal Component Analysis (CP/PCA) appears to produce a more reliable and physically plausible model than an MLR analysis and offers more insight into the environmental and operational driving forces that influence energy consumption in a commercial building. It is thought that the method will be useful for determining conservation retrofit savings from pre-retrofit and post-retrofit consumption data for commercial buildings. A companion paper presents the development of the four-parameter change-point model and a comparison to the Princeton Scorekeeping Method (PRISM) (Ruch and Claridge, 1991).

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