Reducing cooling load and lifecycle cost for residential buildings: a case of Lahore, Pakistan

2021 ◽  
Author(s):  
Huma Khalid ◽  
Muhammad Jamaluddin Thaheem ◽  
Muhammad Sohail Anwar Malik ◽  
Muhammad Ali Musarat ◽  
Wesam Salah Alaloul
Keyword(s):  
Residential Buildings ◽  
Cooling Load

scholarly journals Parametric Design Structures in Low Rise Buildings in Relation to the Urban Context in UAE

2021 ◽  
Vol 13 (15) ◽  
pp. 8595
Author(s):  
Lindita Bande ◽  
Abeer Alshamsi ◽  
Anoud Alhefeiti ◽  
Sarah Alderei ◽  
Sebah Shaban ◽  
...  
Keyword(s):  
Heat Stress ◽  
Parametric Design ◽  
Residential Buildings ◽  
Cost Evaluation ◽  
Outdoor Thermal Comfort ◽  
Climate Index ◽  
Cooling Load ◽  
Design Structure ◽  
Al Ain ◽  
The City

The city of Al Ain (Abu Dhabi, UAE) has a mainly low rise residential buildings. Villas as part of a compound or separate units represent the majority of the residential areas in the city. Due to the harsh hot arid climate of Al Ain, the energy demand for the cooling load is quite high. Therefore, it is relevant finding new retrofit strategies that are efficient in reducing the cooling load of the villas. The aim of this study is to analyze one particular strategy (parametric shading structure) in terms of design, construction, cost, energy impact on the selected villa. The main data for this study is taken from the local sources. There are six steps followed in this analysis: case study analysis; climate analysis; parametric structure and PV panels; building energy consumption and outdoor thermal comfort; modelling, simulation, and validation; materials, construction, and cost evaluation. The model of the villa was validated for the full year 2020 based on the electricity bills obtained. After adding the parametric design structure, the reduction after shading is approximately 10%. Meanwhile the UTCI (Universal Thermal Climate Index) dropped from extreme heat stress to strong heat stress (average for the month of March and September). These findings are promising in the retrofit industry due to the advanced calculations used to optimize the parametric design structure.

scholarly journals A NILM method for cooling load disaggregation based on artificial neural network

2019 ◽  
Vol 111 ◽  
pp. 05020 ◽  
Author(s):  
Ziwei Xiao ◽  
Jiaqi Yuan ◽  
Wenjie Gang ◽  
Chong Zhang ◽  
Xinhua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Management ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Building Energy Management ◽  
Load Disaggregation ◽  
Artificial Neural ◽  
Cooling Loads

The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.

Using Forecasted Daily Maximum Temperatures to Control a Chiller Thermal Storage System

2018 ◽  
Author(s):  
Christopher Baldwin ◽  
Cynthia A. Cruickshank
Keyword(s):  
Storage Tank ◽  
Residential Buildings ◽  
Storage System ◽  
Peak Load ◽  
Cost Savings ◽  
Thermal Storage ◽  
The United States ◽  
Daily Maximum ◽  
Cooling Load ◽  
Control Scheme

Residential buildings in Canada and the United States are responsible for approximately 20% of secondary energy consumption. Over the past 25 years, air conditioning has seen the single largest increase of any residential end use. This load currently places a significant peak load on the electrical grid during later afternoon periods during the cooling season. One method to reduce or eliminate this peak load being placed in the grid is the use of a chiller coupled with a thermal storage system. The chiller operates during off-peak periods, predominately over-night to charge the thermal storage tank, and the stored cooling potential is realized to meet the cooling loads during peak periods. In previous studies, the use of a chiller has seen a reduction in annual operating costs, however a significant increase in energy occurs as a result of decreased performance of the chiller. To improve system performance, a new control scheme was developed, which uses the forecasted daily high for the next day to predict the cooling load for the day during peak periods for the day. The predicted cooling load is then used as the set-point for the cold thermal storage tank, allowing the peak cooling load to be met using stored cooling potential. This control scheme was implemented into a modelled house located in each of the 7 major ASHRAE zones, with a storage tank with a previously found optimal tank volume. Across each of the locations, a reduction in annual utility costs and overall energy required to meet the building loads observed, with the total cost savings between 0.3% and 1.5% and total electricity required to meet the cooling demand decreasing by as much as 10.2%.

Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

2016 ◽  
Vol 819 ◽  
pp. 541-545 ◽  
Author(s):  
Sholahudin ◽  
Azimil Gani Alam ◽  
Chang In Baek ◽  
Hwataik Han
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Simulation Data ◽  
Heating And Cooling ◽  
Heating Load ◽  
Input Variables ◽  
Cooling Loads

Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

"Quantitative estimation of cooling load capabilities of residential buildings using machine learning"

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Nedret Bećirović ◽  
Ismail Bejtović ◽  
Jasmin Kevrić
Keyword(s):  
Machine Learning ◽  
Quantitative Estimation ◽  
Residential Buildings ◽  
Cooling Load ◽  
Wall Area ◽  
Area Distribution ◽  
Roof Area ◽  
Study Support ◽  
Input Variables ◽  
Learning Software

Based on previous research on energy efficiency of the buildings, particularly their cooling load capabilities we will develop a collection of machine learning methods for detecting buildings with best cooling load capabilities. This collection will study the influence of 8 input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) on one output parameter, that is cooling load of buildings. The results of this study support the practicability of using machine-learning software to estimate building parameters as a convenient and accurate approach, as long as the methods chosen are well suited for the type of data in question.

scholarly journals Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

2020 ◽  
Vol 10 (11) ◽  
pp. 3829 ◽  
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Heating And Cooling Load

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

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