Modeling and Forecasting End-Use Energy Consumption for Residential Buildings in Kuwait Using a Bottom-Up Approach

To meet the rapid-growing demand for electricity in Kuwait, utility planners need to be informed on the energy consumption to implement energy efficiency measures to manage sustainable load growth and avoid the high costs of increasing generation capacities. The first step of forecasting the future energy profile is to establish a baseline for Kuwait (i.e., a business-as-usual reference scenario where no energy efficiency incentives were given and the adoption of energy efficient equipment is purely market-driven). This paper presents an investigation of creating a baseline end-use energy profile until 2040 for the residential sector in Kuwait by using a bottom-up approach. The forecast consists of mainly two steps: (1) Forecasting the quantity of the residential energy-consuming equipment in the entire sector until 2040 where this paper used a stock-and-flow model that accounted for the income level, electrification, and urbanization rate to predict the quantify of the equipment over the years until 2040, and (2) calculate the unit energy consumption ( UEC ) for all equipment types using a variety of methods including EnergyPlus simulation models for cooling equipment. By combining the unit energy consumption and quantity of the equipment over the years, this paper established a baseline energy use profile for different end-use equipment for Kuwait until 2040. The results showed that the air conditioning loads accounted for 67% of residential electrical consumption and 72% of residential peak demand in Kuwait. The highest energy consuming appliances were refrigerators and freezers. Additionally, the air conditioning loads are expected to rise in the future, with an average annual growth rate of 2.9%, whereas the lighting and water heating loads are expected to rise at a much lower rate.

[1]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[2]  L. Hunt,et al.  Measuring underlying energy efficiency in the GCC countries using a newly constructed dataset , 2019, Energy Transitions.

[3]  I. Azevedo,et al.  Residential electricity consumption in Portugal: Findings from top-down and bottom-up models , 2011 .

[4]  Noureddine Settou,et al.  Modeling and forecasting energy consumption for residential buildings in Algeria using bottom-up approach , 2016 .

[5]  Omar Khattab,et al.  Occupants’ behavior and activity patterns influencing the energy consumption in the Kuwaiti residences , 2003 .

[6]  Lester C. Hunt,et al.  Modelling residential electricity demand in the GCC countries , 2016 .

[7]  Runa Nesbakken,et al.  Price sensitivity of residential energy consumption in Norway , 1999 .

[8]  Jürgen P. Kropp,et al.  Heating and cooling energy demand and related emissions of the German residential building stock under climate change , 2011 .

[9]  M. A. Rafe Biswas,et al.  Regression analysis for prediction of residential energy consumption , 2015 .

[10]  Ali Hajiah,et al.  Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City , 2017 .

[11]  V. Ismet Ugursal,et al.  Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector , 2008 .

[12]  M. Krarti,et al.  Impact of subsidization on high energy performance designs for Kuwaiti residential buildings , 2016 .

[13]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[14]  Ali Hajiah,et al.  Simulation-based analysis of the use of PCM-wallboards to reduce cooling energy demand and peak-loads in low-rise residential heavyweight buildings in Kuwait , 2017 .

[15]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

[16]  Alam Hossain Mondal,et al.  Market penetration modeling of high energy efficiency appliances in the residential sector , 2017 .

[17]  Atif S. Debs ENERGY CONSERVATION IN KUWAITI BUILDINGS , 1984 .

[18]  V. I. Ugursal,et al.  A residential end‐use energy consumption model for Canada , 1998 .

[19]  Ali Hajiah,et al.  Life cycle building impact of a Middle Eastern residential neighborhood , 2017 .

[20]  Dejan Mumovic,et al.  A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .

[21]  Moncef Krarti,et al.  Evaluation of large scale building energy efficiency retrofit program in Kuwait , 2015 .

[22]  T. Considine The Impacts of Weather Variations on Energy Demand and Carbon Emissions , 2000 .

[23]  Nan Zhou,et al.  Analysis of potential energy saving and CO2 emission reduction of home appliances and commercial equipments in China , 2011 .

[24]  Tarek Atalla,et al.  A Global Degree Days Database for Energy-Related Applications , 2018 .

[25]  International Handbook on the Energy Economics , 2010 .

[26]  Michael A. McNeil,et al.  Modeling diffusion of electrical appliances in the residential sector , 2010 .

[27]  Moncef Krarti,et al.  Analysis of impact of daylight time savings on energy use of buildings in Kuwait , 2011 .

[28]  J. Swisher,et al.  Exploring the gap : Top-down versus bottom-up analyses of the cost of mitigating global warming , 1993 .

[29]  M. Elkhafif An iterative approach for weather-correcting energy consumption data , 1996 .

[30]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .