Household electricity demand forecast and energy savings potential for Vientiane,Lao PDR

This paper uses the survey data on household electricity demand from five districts of Vientiane, Lao PDR, for the demand projection up to 2030 using the end-use model. The scenario analysis is used to verify the potential of an energy-saving program by alternating selected appliances with more energy-efficient ones following the labelling standard of Thailand. The demographic structure of electrified households and the energy efficiency of electric appliances are considered as the dominant factors affecting electricity demand. Under the base-case scenario, the total electricity demand of Vientiane increased from 593?GWh in 2013 to 965?GWh in 2030. In the energy efficiency scenario, it is revealed that the appliance standard enhancement program can save total electricity demand in 2030 by 147?GWh (?15.2%), where 117?GWh (?12.1%) of which is contributed by the air conditioner and 30?GWh (?3.1%) by the lighting equipment.     

Modelling and simulation of a distributed power generation system with energy storage to meet dynamic household electricity demand

Electrical consumption in a household is not stable but changeable in one day throughout a whole year. The consumption depends on weather, seasons and users. This characteristic of demand makes it difficult to design and build a distributed power generation system to meet the demand for a household. For this reason, a stand-alone distributed power generation system (DPGS) needs to be carefully designed not only to meet the dynamic household electricity demand, but also to be economical. Hence, for a DPGS, it is essential to utilise electrical energy storage (EES) unit to store the excessive energy while power generation is running at off-peak time; and then the EES may supply the stored energy during the peak demand period. This study investigates a distributed power generation system with an electric energy storage unit to meet the dynamic electricity demand in a household. The system composes of one diesel-engine-generator (DG) running with biofuel; a fuel cell; integrated with an energy storage unit including a supercapacitor and a group of batteries. Models have been set up in Dymola software and two different system configurations are proposed and simulated. The characteristics of the integrated DPGS–EES system are presented and discussed. The results show that both configurations are working properly to meet the demand.     

Demographics,energy and our homes

The projected growth in households in the UK is a key factor in future domestic energy consumption, particularly electricity consumption. While every household needs a home and its heating, lighting and appliances, increasing incomes have historically led to significantly higher appliance ownership, higher expectations of levels of energy service and greater usage. In the past this trend was combined with increasing household numbers to drive growth in domestic electricity demand. Official projections for population growth and household composition indicate significant drivers for future growth in energy demand. Curbing this will require policies to reverse the tendency for energy–efficiency improvements to be overwhelmed by growing numbers of households, more widespread appliance ownership and increased service expectations.     

Modelling of electricity savings in the Danish households sector: from the energy system to the end-user

In this paper, we examine the value of investing in energy-efficient household appliances from both an energy system and end-user perspectives. We consider a set of appliance categories constituting the majority of the electricity consumption in the private household sector, and focus on the stock of products which need to be replaced. First, we look at the energy system and investigate whether investing in improved energy efficiency can compete with the cost of electricity supply from existing or new power plants. To assess the analysis, Balmorel, a linear optimization model for the heat and power sectors, has been extended in order to endogenously determine the best possible investments in more efficient home appliances. Second, we propose a method to relate the optimal energy system solution to the end-user choices by incorporating consumer behaviour and electricity price addition due to taxes. The model is non-exclusively tested on the Danish energy system under different scenarios. Computational experiments show that several energy efficiency measures in the household sector should be regarded as valuable investments (e.g. an efficient lighting system) while others would require some form of support to become profitable. The analysis quantifies energy and economic savings from the consumer side and reveals the impacts on the Danish power system and surrounding countries. Compared to a business-as-usual energy scenario, the end-user attains net economic savings in the range of 30–40 EUR per year, and the system can benefit of an annual electricity demand reduction of 140–150 GWh. The paper enriches the existing literature about energy efficiency modelling in households, contributing with novel models, methods, and findings related to the Danish case.     

HIES: Cases for hydrogen energy and I-Energy

With the increasing demand for electricity, more and more fossil fuels are used to generate electricity which leads to energy shortage and environmental pollution. Therefore, using Renewable Energy Sources (RESs) and developing sustainable smart grid have become a common global priority: Since RESs, like solar and wind energy, are inherently unstable, hydrogen energy, as a completely clean new energy, has received widespread attention: I-Energy, which combines information and energy, is a new research direction in smart grid. Furthermore, the household electricity usage accounts for 41% of the total power consumption. Therefore, Household Intelligent Electricity System (HIES), combining hydrogen energy and i-Energy, becomes smart solutions. In this paper, we review the scientific literature for hydrogen energy and i-Energy on HIES, including recognition of electricity appliances, establishment of power consumption model, human activity analysis, smart interactive terminal, home energy management system, distributed power supply and district coordinated power utilization. Finally, we summarize the challenges and give the solutions concerning HIES, and this work can give a useful reference to new energy used model and environment protection.     

Artificial neural networks for energy analysis of office buildings with daylighting

An artificial neural network (ANN) model was developed for office buildings with daylighting for subtropical climates. A total of nine variables were used as the input parameters – four variables were related to the external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), four for the building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections), and the last variable was day type (i.e. weekdays, Saturdays and Sundays). There were four nodes at the output layer with the estimated daily electricity use for cooling, heating, electric lighting and total building as the output. Building energy simulation using EnergyPlus was conducted to generate daily building energy use database for the training and testing of ANNs. The Nash–Sutcliffe efficiency coefficient for the ANN modelled cooling, heating, electric lighting and total building electricity use was 0.994, 0.940, 0.993, and 0.996, respectively, indicating excellent predictive power. Error analysis showed that lighting electricity use had the smallest errors, from 0.2% under-estimation to 3.6% over-estimation, with the coefficient of variation of the root mean square error ranging from 3% to 5.6%.     

Efficient heat utilization of a space cooling system over a range of atmospheric temperatures and solar radiation

Behavior of a space cooling system using the diurnal range of atmospheric temperatures in the summer is examined by numerical simulation. The system consists of radiators, a thermal energy storage tank, fan‐coil coolers, and an electric cooler. Conditions for the calculation were obtained from actual weather records and characteristics of appliances. Total consumption of electric power was used for evaluating the system performance. The larger the volume of the storage tank becomes, the smaller the consumption. The consumption is smaller when the radiator is operated for a period of some limited hours than when operating through the night. To decrease the consumption, there are optimum values of the flow rate of the pumps and in the on–off temperature of the fan‐coil cooler. Stratified tanks could reduce the consumption compared with mixed ones. This system has the possibility of reducing demand for electricity or of shifting the demand to off‐peak hours. © 2001 Scripta Technica, Heat Trans Asian Res, 30(2): 126–138, 2001     

Air conditioning in the region of Madrid, Spain: An approach to electricity consumption, economics and CO2 emissions

An understanding of electricity consumption due to residential air conditioning (AC) may improve production and environmental impact strategy design. This article reports on a study of peak and seasonal electricity consumption for residential air conditioning in the region of Madrid, Spain. Consumption was assessed by simulating the operation of AC units at the outdoor summer temperature characteristics of central Spain. AC unit performance when operating under part load conditions in keeping with weather conditions was also studied to find cooling demand and energy efficiency. Likewise final electricity consumption was computed and used to calculate energy costs and greenhouse gas emissions (GHGs). Cooling demand, when family holidays outside the region were factored into the calculations, came to 1.46 × 10⁹ kWh. Associated seasonal electricity demand was 617 × 10⁶ kWh and seasonal performance of AC units around 2.4. Electricity consumption in the whole region was observed to peak on 30 June 2008 at 5.44 × 10⁶ kW, being the load attributable to residential AC 1.79 × 10⁶ kW, resulting about 33% of the total peak consumption. The seasonal cost per household was about €156 and the total equivalent warming impact was 572 × 10³ t CO₂. The method proposed can be adapted for use in other regions.     

智能电网下多时间尺度家庭能量管理优化策略

建立负荷在功率约束与需求响应约束下的激励需求响应模型以及含分布式电源、储能与电动汽车的家庭用电模型,在预测模型多时间尺度能量管理的基础上,以最小化用户自身用电费用与买电功率波动的两层目标函数实时优化调整策略。通过实时调整储电池、电动汽车的充放电,从而保证用户购电满足需求相应的要求。最后采用改进的粒子群算法对多时间尺度目标函数进行求解,并且与原始的粒子群算法进行对比,结果表明所提算法可显著降低用户的用电费用与功率波动。     

Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches

This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting in Lebanon. In order to provide the forecasted energy consumption, the ANN interpolates among the EEC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on EEC time series and a weather‐dependent variable, namely, degree days (DD). The third model is also a multivariate model based on EEC and a gross domestic product (GDP) proxy, namely, total imports (TI). Finally, the fourth model combines EEC, DD and TI. Forecasting performance measures such as mean square errors (MSE), mean absolute deviations (MAD), mean percentage square errors (MPSE) and mean absolute percentage errors (MAPE) are presented for all models. Copyright © 2002 John Wiley & Sons, Ltd.     

A realistic energy optimization model for smart‐home appliances

Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. The control of smart appliances can be based on optimization models, which should be realistic and efficient. However, increased realism also implies an increase in solution time. Many of the optimization models in the literature have limitations on the types of appliances considered and/or their reliability. This paper proposes a home energy management scheduling model that is more realistic and efficient. We develop a mixed integer linear optimization model that minimizes the energy cost while maintaining a given level of user comfort. Our main contribution is the variety of specific appliance models considered and their integration into a single model. We consider the use of energy in appliances and electric vehicles (EVs) and take into account renewable local generation, batteries, and demand response. Our models of a shower, a fridge, and a hybrid EV consider both the electricity consumption and the conventional fuel cost. We present computational results to validate the model and indicate how it overcomes the limitations of other models. Our results, compared with the best competitors, provide cost savings ranging from 8% to 389% over a horizon of 24 hours.     

基于需求响应的居民可控能效负荷优化

为解决居民生活用电需求不断增加,居民生活用电方式不合理造成能源浪费越来越严重的问题,从居民用户的可控能效负荷入手,对典型可控能效负荷空调、热水器和照明负荷进行分析,建立负荷能耗数学模型,根据其运行特性,结合居民用户用电习惯和分时电价,制定居民可控能效负荷优化策略;建立以居民用户用电成本和用电满意度为目标的优化模型。为提高和声搜索算法的求解速度与计算精度,对其参数进行动态调整,并与差分进化算法进行融合,应用于可控能效负荷的优化求解。算例结果表明了改进算法具有较好的收敛性和较高的准确性,验证了居民可控能效负荷优化策略的可行性,实现了从需求响应的角度对可控能效负荷进行优化管理的思想。     

Contribution to the analysis of urban residential electrical energy demand in developing countries

Access of large segments of the population to electrical energy continues to be limited in the cities of developing countries where the standards pertaining to installed power and consumption are ill suited to the level of appliances owned by households. In this paper, a new evaluation method of residential users’ actual needs in electricity is proposed for Cameroon. The approach is based on the determination of the nature, number and power of the appliances, as well as their operation periods. The method used is a survey administered to a representative sample of households. A detailed questionnaire appraises specific consumption and installed power and is also used to construct the households’ load curve. The need for electrical energy varies according to the size of the city and, inside the city, to household income. The average power used per dwelling accounts for less than half the value prescribed by current norms. If implemented, the method would help optimise network investments and operation. The load curve obtained in the experiment shows that the peak output power is recorded in the evening and that household demand makes up most of this charge. The proposed strategy would reduce electrical energy costs, thus being of great potential interest for poor urban populations.     

Post-Kyoto energy consumption strategies for the Greek interconnected electric system

The liberalization of the Greek electric market (Law 2773/99, updated with Laws 3175/2003 and 3426/2005 for incorporating Directive 2003/54 into the Greek legislation) is in its final structural transformation, which includes the fact that from 1.7.2007 each customer can select its electricity provider. This new status together with the procedure towards the formation of a post-Kyoto plan, raise the need of examining different energy saving strategies in the consumption side for evaluating their economic and environmental consequences. Such strategies may be useful for the decision makers or the electricity retail companies. This paper examines the influence of several post-Kyoto electricity consumption strategies in the Greek interconnected electric system for the period 2005–2025. The aim of the paper is to be used as a decision makers’ tool for investigating the potential of electricity consumption policies. The results show that policies related either to seasonal peak demand control, or targeting at the total electric consumption lead to significant gains and emission reduction. Moreover the influence of factors, such as the weather conditions, the discount rate of the energy investments, the fuel prices evolution and the consumers’ behavior linkage with oil prices are examined.     

Energy efficiency standards for refrigerators in Brazil: A methodology for impact evaluation

In Brazil energy efficiency standards for cold appliances was established in 2007. A specified single set of MEPS (minimum energy performance standards) for refrigerators, freezers and freezer refrigerators was implemented without evaluating its impacts and estimation of potential electricity savings. This paper presents a methodology for assessing the impacts of the Brazilian MEPS for cold appliances. It uses a bottom-up approach to estimate residential end-use consumption and to evaluate the energy saving potential for refrigerators. The household electricity consumption is projected by modeling appliance ownership using an econometric approach based on the recent household survey data. A cost–benefit analysis for more stringent standards is presented from the perspective of the society and electricity customers. The results showed that even considering the current market conditions (high discount rate for financing new efficient equipment) some MEPS options are advantageous for customers. The analysis also demonstrates significant cost-effective saving potential from the society perspective that could reach 21 TWh throughout the period of 2010–2030—about 25% of current residential consumption.     

Energy and associated greenhouse gas emissions from household appliances in Malaysia

Today, electricity is an indispensable key for civilization and development. The trend of electricity consumption is rather escalating. Electricity generation principally depends upon fossil fuels. In one hand, the stocks of these fuels have been confirmed to be critically limited. On the other hand, in process of electricity generation by means of these fuels, a number of poisonous by-products adversely affect the conservation of natural eco-system. Further, electricity driven appliances use emanate anti-environmental gases that also affect human health and climate. Therefore, estimation of energy consumption for operating household appliances, savings of energy under policy intervention, and emission of poisonous gases in a fast developing country deserve academic attention.     

Electricity end-uses in the residential sector of Brazil

The residential energy consumption has been studied in many countries as it usually accounts for a large percentage of the total energy consumption. Energy end-uses have also been a matter of concern as they can assist energy system planning. The objective of this paper is to assess the actual scenario of electricity consumption and estimate electricity end-uses in the residential sector of Brazil for different bioclimatic zones. The analysis is based on a survey performed by 17 energy utilities enclosing a total of 17,643 houses or flats over 12 states in Brazil. The survey was performed to obtain electricity consumption data for all household appliances found in houses and flats. The electricity end-uses were estimated by performing weighted averages according to the location of the dwellings in each bioclimatic zone. Results indicate that the largest end-uses are for refrigerator and freezer together, which account for about 38–49% of the electricity consumption in dwellings in Brazil. Air-conditioning and electric shower are the end-uses that are more dependent on the climatic conditions. The main conclusion that can be made from the analysis is that air-conditioning should be a major concern in the residential sector of Brazil in the near future as its ownership is still low, but its electricity consumption is already significant mainly over summer.     

Using machine learning tools for forecasting natural gas consumption in the province of Istanbul

Commensurate with unprecedented increases in energy demand, a well-constructed forecasting model is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas-consuming mega-city. These tools include multiple linear regression (MLR), an artificial neural network approach (ANN) and support vector regression (SVR). The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time-periods.     

Prediction of greenhouse gas reduction potential in Japanese residential sector by residential energy end-use model

A model is developed that simulates nationwide energy consumption of the residential sector by considering the diversity of household and building types. Since this model can simulate the energy consumption for each household and building category by dynamic energy use based on the schedule of the occupants’ activities and a heating and cooling load calculation model, various kinds of energy-saving policies can be evaluated with considerable accuracy. In addition, the average energy efficiency of major electric appliances used in the residential sector and the percentages of housing insulation levels of existing houses is predicted by the “stock transition model.” In this paper, energy consumption and CO₂ emissions in the Japanese residential sector until 2025 are predicted. For example, as a business – as-usual (BAU) case, CO₂ emissions will be reduced by 7% from the 1990 level. Also evaluated are mitigation measures such as the energy efficiency standard for home electric appliances, thermal insulation code, reduction of standby power, high-efficiency water heaters, energy-efficient behavior of occupants, and dissemination of photovoltaic panels.