The impact of natural disasters on energy consumption: An analysis of renewable and nonrenewable energy demand in the residential and industrial sectors

2017 ◽  
Vol 37 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Nadia Doytch ◽  
Yehuda L. Klein
Keyword(s):  
Energy Consumption ◽  
Natural Disasters ◽  
Energy Demand ◽  
Industrial Sectors ◽  
The Impact ◽  
Nonrenewable Energy

The impact of technology on architecture in Iran with focus on saving in energy consumption

2015 ◽  
Vol 16 (SE) ◽  
pp. 97-103
Author(s):  
Allah Bakhsh Kavoosi ◽  
Shahin Heidari ◽  
Hamed Mazaherian
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Industrial Revolution ◽  
Demand Prediction ◽  
Interior Spaces ◽  
Heating And Cooling ◽  
Impact Of Technology ◽  
Contemporary Architecture ◽  
The Impact ◽  
The Given

Growth and development of technology caused enormous transformation and change in the world after Industrial Revolution. The contemporary human has prepared the platform for their realization in many activities that the humans were unable to do it in the past time and struck the dream of their realization in their mind so that today doing many of those activities have been apparently practical by human. This accelerating growth accompanied with consuming a lot of energy where with respect to restriction of the given existing resources, it created energy crises. On the other hand, along with growth in industry and requirement for manpower and immigration from village to city and basic architectural changes in houses, which have emerged due to change in social structure it has led to change in lifestyle and type and quantity of consuming energy in contemporary architecture. Inter alia, with increase in human’s capability, cooling and heating and acoustic and lighting technologies were also changed in architecture and using mechanical system was replaced by traditional systems. Application of modern systems, which resulted from growth of industry and development of technology and it unfortunately, caused further manipulation in nature and destruction of it by human in addition to improving capability and potential of human’s creativity. With respect to growth of population and further need for housing and tendency to the dependent heating and cooling systems to them in this article we may notice that the housing is assumed as the greatest consumer of energy to create balance among the exterior and interior spaces in line with creating welfare conditions for heating and cooling and lighting. The tables of energy demand prediction in Iran show that these costs and energy consumption will be dubbed with energy control smart management in architecture.

scholarly journals The Impact of Individual Behavior on Household Energy Saving

2021 ◽  
Vol 3 (1) ◽  
pp. 39-46
Author(s):  
Gulshan Maqbool ◽  
Zulqarnain Haider
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Influencing Factors ◽  
Energy Demand ◽  
Energy Use ◽  
Household Head ◽  
Least Square ◽  
Primary Data ◽  
Saving Behavior ◽  
The Impact

Energy-saving behaviors are defined as the daily and habitual practices of households that focus on specific reductions in energy use. The main objective of this research was to estimate the impact of the energy-saving behavior of individuals on energy demand and to estimate the impact of factors affections the adoption of energy-saving techniques. The study is based on primary data which is collected through questionnaires. The data were collected from rural and urban households in four tehsils of district Sargodha, Pakistan. The Ordinary Least Square technique was to describe the relationship between electricity consumption and different explanatory variables such as gender, age, region, family members, dwelling area, income, energy consumption awareness, external influencing factors, and household saving behavior. Job status is negative and significant, qualification variable in this study is insignificant, marital status is negatively associated with energy consumption and significant, size of a household has a significant effect on the model.  The monthly income of the household head has a positive and significant effect. Energy consumption awareness is significantly negative. External influencing factors are insignificant. Saving behavior in electronic appliances is significantly negative to energy consumption. Government should put efforts to aware the public about energy-saving measures through an awareness campaign using electronic media like mobile and email. Energy-saving appliances should be a sale at cheap prices. The household should have to change its habitual behavior.

scholarly journals The Effect of Scale, Technique, Composition and Trade Openness on Energy Demand: Fresh Evidence from Malaysia

2019 ◽  
Author(s):  
Muhammad Shahbaz ◽  
Md. Mahmudul Alam ◽  
Gazi Salah Uddin ◽  
Loganathan Nanthakumar
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Structural Breaks ◽  
Negative Impact ◽  
Trade Openness ◽  
Growth Effect ◽  
Demand Model ◽  
Bounds Testing ◽  
Long Run ◽  
The Impact

The aim of this paper utilizes an energy demand model to investigate the impact of trade openness on energy consumption by incorporating scale and technique, composition and urbanization effects in the case of Malaysia. The study covers the sample period of 1970-2011 using quarter frequency data. We applied the bounds testing approach in the presence of structural breaks to examine the long run relationship between the variables. The VECM Granger causality is used to detect the direction of causality between the variables. Our findings indicate that growth effect (scale and technique effect) has a positive (negative) impact on energy consumption whereas composition effect stimulates energy demand in Malaysia.. Energy consumption is positively influenced by both from openness and urbanization. This study opens new policy insights for policy making authorities to articulate a comprehensive energy and trade policy to sustain economic growth and improve the environmental quality of Malaysia.

scholarly journals Impact of Green Roofs on Energy Demand for Cooling in Egyptian Buildings

2020 ◽  
Vol 12 (14) ◽  
pp. 5729 ◽  
Author(s):  
Ayman Ragab ◽  
Ahmed Abdelrady
Keyword(s):  
Thermal Conductivity ◽  
Energy Consumption ◽  
Energy Demand ◽  
Soil Depth ◽  
Green Roof ◽  
Green Roofs ◽  
Climatic Regions ◽  
Reduce Energy Consumption ◽  
The Impact ◽  
Reducing Energy

Energy consumption for cooling purposes has increased significantly in recent years, mainly due to population growth, urbanization, and climate change consequences. The situation can be mitigated by passive climate solutions to reduce energy consumption in buildings. This study investigated the effectiveness of the green roof concept in reducing energy demand for cooling in different climatic regions. The impact of several types of green roofing of varying thermal conductivity and soil depth on energy consumption for cooling school buildings in Egypt was examined. In a co-simulation approach, the efficiency of the proposed green roof types was evaluated using the Design-Builder software, and a cost analysis was performed for the best options. The results showed that the proposed green roof types saved between 31.61 and 39.74% of energy, on average. A green roof featuring a roof soil depth of 0.1 m and 0.9 W/m-K thermal conductivity exhibited higher efficiency in reducing energy than the other options tested. The decrease in air temperature due to green roofs in hot arid areas, which exceeded an average of 4 °C, was greater than that in other regions that were not as hot. In conclusion, green roofs were shown to be efficient in reducing energy consumption as compared with traditional roofs, especially in hot arid climates.

scholarly journals Green Roofs as Effective Tools for Improving the Indoor Comfort Levels of Buildings—An Application to a Case Study in Sicily

2020 ◽  
Vol 10 (3) ◽  
pp. 893 ◽  
Author(s):  
Laura Cirrincione ◽  
Maria La Gennusa ◽  
Giorgia Peri ◽  
Gianfranco Rizzo ◽  
Gianluca Scaccianoce ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Electric Energy ◽  
Simulation Models ◽  
Green Roofs ◽  
Energy Performance ◽  
Comfort Levels ◽  
Indoor Comfort ◽  
The Impact

In the line of pursuing better energy efficiency in human activities that would result in a more sustainable utilization of resources, the building sector plays a relevant role, being responsible for almost 40% of both energy consumption and the release of pollutant substances in the atmosphere. For this purpose, techniques aimed at improving the energy performances of buildings’ envelopes are of paramount importance. Among them, green roofs are becoming increasingly popular due to their capability of reducing the (electric) energy needs for (summer) climatization of buildings, hence also positively affecting the indoor comfort levels for the occupants. Clearly, reliable tools for the modelling of these envelope components are needed, requiring the availability of suitable field data. Starting with the results of a case study designed to estimate how the adoption of green roofs on a Sicilian building could positively affect its energy performance, this paper shows the impact of this technology on indoor comfort and energy consumption, as well as on the reduction of direct and indirect CO2 emissions related to the climatization of the building. Specifically, the ceiling surface temperatures of some rooms located underneath six different types of green roofs were monitored. Subsequently, the obtained data were used as input for one of the most widely used simulation models, i.e., EnergyPlus, to evaluate the indoor comfort levels and the achievable energy demand savings of the building involved. From these field analyses, green roofs were shown to contribute to the mitigation of the indoor air temperatures, thus producing an improvement of the comfort conditions, especially in summer conditions, despite some worsening during transition periods seeming to arise.

scholarly journals Analysis of the Impact of Different Variables on the Energy Demand in Office Buildings

2020 ◽  
Vol 12 (13) ◽  
pp. 5347
Author(s):  
José Luis Fuentes-Bargues ◽  
José-Luis Vivancos ◽  
Pablo Ferrer-Gisbert ◽  
Miguel Ángel Gimeno-Guillem
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Energy Use ◽  
Computer Software ◽  
Energy Performance ◽  
Point Of View ◽  
Regulatory Systems ◽  
Energy Behaviour ◽  
Balance Energy ◽  
The Impact

The design of near zero energy offices is a priority, which involves looking to achieve designs which minimise energy consumption and balance energy requirements with an increase in the installation and consumption of renewable energy. In light of this, some authors have used computer software to achieve simulations of the energy behaviour of buildings. Other studies based on regulatory systems which classify and label energy use also generally make their assessments through the use of software. In Spain, there is an authorised procedure for certifying the energy performance of buildings, and software (LIDER-CALENER unified tool) which is used to demonstrate compliance of the performance of buildings both from the point of view of energy demand and energy consumption. The aim of this study is to analyse the energy behaviour of an office building and the variability of the same using the software in terms of the following variables: climate zone, building orientation and certain surrounding wall types and encasements typical of this type of construction.

scholarly journals Prediction of Cooling Energy Consumption Using a Neural Network on the Example of the Hotel Building

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 21
Author(s):  
Marek Borowski ◽  
Klaudia Zwolińska
Keyword(s):  
Energy Consumption ◽  
Multilayer Perceptron ◽  
Energy Demand ◽  
Weather Conditions ◽  
Demand Model ◽  
Factors Affecting ◽  
Actual Energy Consumption ◽  
Indoor Comfort ◽  
The Impact ◽  
Cooling Energy Demand

The purpose of this work is to determine internal and external factors affecting the cooling energy demand of a building. During the research, the impact of weather conditions and the level of hotel occupancy on cooling energy, which is necessary to obtain indoor comfort conditions, was analyzed. The subject of research is energy consumption in the Turówka hotel located in Wieliczka (southern Poland). In the article, the designer of neural networks was used in the Statistica statistical package. To design the network, a widely used multilayer perceptron model with an algorithm with backward error propagation was used. Based on the collected input and output data, various multilayer perceptron (MLP) networks were tested to determine the relationship most accurately reflecting actual energy consumption. Based on the results obtained, factors that significantly affect the consumption of thermal energy in the building were determined, and a predictive energy demand model for the analyzed object was presented. The result of the work is a forecast of cooling energy demand, which is particularly important in a hotel facility. The prepared predictive model will enable proper energy management in the facility, which will lead to reduced consumption and thus costs related to facility operation.

scholarly journals Forecasting peak energy demand for smart buildings

2020 ◽  
Author(s):  
Mona A. Alduailij ◽  
Ioan Petri ◽  
Omer Rana ◽  
Mai A. Alduailij ◽  
Abdulrahman S. Aldawood
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Energy Consumption ◽  
Energy Demand ◽  
Arima Model ◽  
Demand Forecasting ◽  
User Preferences ◽  
Machine Learning Techniques ◽  
Peak Energy ◽  
The Impact

AbstractPredicting energy consumption in buildings plays an important part in the process of digital transformation of the built environment, and for understanding the potential for energy savings. This also contributes to reducing the impact of climate change, where buildings need to increase their adaptability and resilience while reducing energy consumption and maintain user comfort. The use of Internet of Things devices for monitoring and control of energy consumption in buildings can take into account user preferences, event monitoring and building optimization. Detecting peak energy demand from historical building data can enable users to manage their energy use more efficiently, while also enabling real-time response strategies (including control and actuation) to known or future scenarios. Several statistical, time series, and machine learning techniques are proposed in this work to predict electricity consumption for five different building types, by using peak demand forecasting to achieve energy efficiency. We have used several indigenous and exogenous variables with a view to test different energy forecasting scenarios. The suggested techniques are evaluated for creating predictive models, including linear Regression, dynamic regression, ARIMA time series, exponential smoothing time series, artificial neural network, and deep neural network. We conduct the analysis on an energy consumption dataset of five buildings from 2014 until 2019. Our results show that for a day ahead prediction, the ARIMA model outperforms the other approaches with an accuracy of 98.91% when executed over a 168 h (1 week) of uninterrupted data for five government buildings.

Sign in / Sign up

Export Citation Format

Share Document