Domestic Electricity Consumption Research Articles

KAJIAN KESIAPAN (E-READINESS) KECAMATAN DALAM PENERAPAN E-GOVERNMENT KOTA SEMARANG

The climate of Semarang that caracterized by dry and hot temperature,low humidity causes the use of air conditioning system in the residential building to obtain indoor thermal comfort. The rise of electricity rate will give a significant effect to the society, the strategy of domestic electricity consumption to be more effeicient is needed. The Government must make a policy as the Goverment Goodwill to realize the attention of electricity consumption efficiency as the experience of the neighbouring country, Malaysia. The efficiency of elecricity consumption was applied in the vision of City in the Garden. It is the government obligation to exemplify the pattern of energi saving to solve the issueof energi crisis. Related previous studies proved building design caused a squanderring of electricity consumption that harmed the environment. One of environmental friendly elements on building design is the application of greenwall.The initial experiment of this study carried out by creeping the plant up the wall of building model. The temperatur of building walls was hourly measured along the day. The result of this study found that creeped plant up the wall building decrease 20C of the indoor temperature. The application of greenwall showed the indoor temperature was lower the outdoor. The second result found that the decrease of indoor temperature was also influence by the planted area. The most effective place of the planted area is in middle of vertical wall.

Does living alone increase the consumption of social resources?

China's living arrangement has changed as the economy grows and society makes progress. More and more people prefer to live alone. In 2018, a total of 240 million people chose to live alone in China, with an annual growth of 0.53% per year. How will the growing number of people living alone affect the resources consumption and the ecological environment? Based on the data from 1998 to 2017 at provincial level, this paper selects domestic water and electricity consumption to represent resources consumption, and household garbage generation to represent ecological environment, taking the proportion of single-person households in the total households as the explanatory variable and age, education, and household appliances as the control variables. This paper aims to apply dynamic panel models to analyze the impact of solitary population on resources consumption (water and electricity resources as representatives) and on waste generation. The results show that (1) people living alone consume more resources and generate more garbage, while household waste is influenced most, followed by household electricity consumption and household water consumption, (2) positive relations between age and resources consumption and waste generation have been identified, and (3) the energy-saving technology of home appliances is conducive to resources conservation and emission reduction.

Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data

As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992–2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992–2019 based on our calibrated nighttime light data.

Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential Building.

Buildings are considered to be one of the world's largest consumers of energy. The productive utilization of energy will spare the accessible energy assets for the following ages. In this paper, we analyze and predict the domestic electric power consumption of a single residential building, implementing deep learning approach (LSTM and CNN). In these models, a novel feature is proposed, the “best N window size” that will focus on identifying the reliable time period in the past data, which yields an optimal prediction model for domestic energy consumption known as deep learning recurrent neural network prediction system with improved sliding window algorithm. The proposed prediction system is tuned to achieve high accuracy based on various hyperparameters. This work performs a comparative study of different variations of the deep learning model and records the best Root Mean Square Error value compared to other learning models for the benchmark energy consumption dataset.

Forecasting Electricity Consumption and Production in Smart Homes through Statistical Methods

• Methods for forecasting energy demand and production are proposed. • Predictions contribute to balance and smoothen the electricity intake from the power grid. • Experimental evaluation is performed on data recorded on a real energy-management system. • The ARIMA and TBATS models are configured and then compared with other forecasting methods. Over the last years, a steady increase in both domestic electricity consumption and in the adoption of personal clean energy production systems has been observed worldwide. By analyzing energy consumption and production on photovoltaic panels mounted in a house, this work focuses on finding patterns in electrical energy consumption and devising a predictive model. Our goal is to find an accurate method to predict electrical energy consumption and production. Being able to anticipate how consumers will use energy in the near future, homeowners, companies and governments may optimize their behavior and the import and export of electricity. We evaluated the ARIMA and TBATS statistical prediction methods and compared them with other models on datasets from a household equipped with photovoltaics and an energy management system. The evaluation results have shown a mean absolute error of 73.62 Watts for the TBATS model, which is far better than the one obtained with neural forecasting methods.

Evaluating the Ability of NOAA-20 Monthly Composite Data for Socioeconomic Indicators Estimation and Urban Area Extraction

The new visible infrared imaging radiometer suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 satellite (NOAA-20) is capable of imaging the Earth during both daytime and nighttime. The NOAA-20 VIIRS’ day/night band (DNB) enables a new generation of nighttime imaging applications. However, few studies investigated the ability of NOAA-20 DNB nighttime light data in modeling socioeconomic indicators [such as the gross domestic product (GDP) and electric power consumption (EPC)] and extracting urban areas. In this article, we first used a simple linear regression model to investigate the potential of NOAA-20 nighttime light data for estimating GDP and EPC at multiple scales; we then extracted urban areas from NOAA-20 nighttime light data by using support vector machines. Taking mainland China as the study area, we found that the correlation coefficient of determination <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> between NOAA-20 nighttime light data and GDP/EPC at both provincial and prefectural scales are higher than 0.7. By comparing the results with NPP-VIIRS nighttime light data, similar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values were obtained at both two scales, indicating that NOAA-20 nighttime light data and NPP-VIIRS data are comparable in estimating socioeconomic indicators. Moreover, NOAA-20 also shows a similar detection ability with NPP-VIIRS in extracting urban areas. This article demonstrated that NOAA-20 and NPP-VIIRS are comparable in economic statistics estimation and urban area extraction. The NOAA-20 nighttime light data can be a useful data source for enlightening more applications in the fields of socioeconomic and urban studies.

Exploring domestic energy consumption feedback through interactive annotation

We report on a three-week field study in which participants from nine households were asked to annotate their domestic electricity consumption data using a prototype interactive visualisation. Through an analysis of the annotations and semi-structured interviews, our findings suggest that the intervention helped participants to develop a detailed and accurate understanding of their electricity consumption data. Our results suggest that energy data visualisations can be improved by having users actively manipulate and annotate their data, as doing so encourages reflection on how energy is being used, facilitating insights on how consumption can be reduced. One of the key findings from our thematic analysis was that participants went beyond the data in their reflections, talking about generational issues, upbringing, financial matters, socio-economic comparisons, environmental concern, mistrust towards utilities, convenience, comfort and self-reported waste. Reading beyond the data illustrates the importance of social practices in the context of energy feedback, embedding eco-feedback research into the relevant context of sociology and psychology research.

Mathematical modeling of sustainable development goals of India agenda 2030: a Neutrosophic programming approach

Since 2015, the United Nations sustainable development goals (SDGs) agenda 2030 has been designed with 17 goals, 169 targets, and 232 unique indicators to help address environmental, development, and sustainability issues globally. India, like other developing nations, desired to achieve its vision 2030 targets. Several authors studied India’s SDGs with different approaches. However, none of the studies explores the concept of neutrosophic programming (NP). It is against this drawback; this study presents an optimization model for India’s socio-economic and environmental goals based on the NP concept. The NP model is capable of handling indeterminacy in optimization-related problems for which other techniques do not. The formulated models simultaneously optimized the gross domestic product (GDP) growth, electricity consumption, and greenhouse gas (GHG) emissions. The solutions revealed that the degree of satisfaction for the goals related to GDP, electricity consumption, and GHG emissions could be achieved partially. Also, it suggested the required optimal number of employment for each economic sector to achieve India’s vision 2030. The SDGs model was further solved with the goal programming (GP) technique and compared with the NP results to validate the proposed concept. It has been found that the proposed model gives a better compromise solution than the GP model. The study can help and guide policymakers in working toward vision 2030 attaintment. Other interested researchers can use the concept in other countries to help decision-makers understand managerial policy implications.

Occupancy analysis in commercial building cooling energy modelling with domestic water and electricity consumption

Occupancy is known to play a crucial role in building energy consumption, but its application in building energy calculations has been much simplified. In whole-building energy statistical models, occupancy is rarely considered if at all. One hurdle is that many buildings in which energy efficiency projects are implemented do not have occupancy sensors for pre-project measurement. This work explores a potential workaround for such buildings by comparing the consumption of domestic cold water and electricity as proxies for occupancy in building energy statistical models. They are tested using a clustering approach and a linear approach. Using the chilled water consumption data of a classroom building, the use of domestic cold water in the clustering approach was able to reduce the model’s coefficient of variation of the root mean square error (CV-RMSE) by 2.9 percentage points to an average of 11.3% from the already-good 14.2% produced by the traditional weekday-and-weekend method. The case study data also covered a period when the COVID-19 pandemic forced the building closure. This sudden change of utility consumption caused problems for all modelling methods and this study highlights the special care that must be taken to treat such events.

The Effect of the COVID-19 Pandemic on the Electricity Consumption in Romania

The COVID-19 pandemic obliged the Romanian government to take drastic measures to contain the virus. More than this, they imposed the heaviest restrictions in the EU. For more than a month, during the lockdown period, everything stopped: schools and universities had only online classes, national and international flights and gatherings were forbidden, and many restrictions for travel were imposed. This paper analyzes the changes that occurred in electricity consumption linked with economic growth, during the pandemic, in Romania. For a better understanding of the correlations between gross domestic product (GDP) and electricity consumption (EC) in different economic contexts, the period 2008–2020 was divided into three series: the 2008–2012 financial crisis and the post-crisis recovery period, the 2013–2019 period of economic growth, and the Q1–Q3 2020 pandemic period. Using correlation coefficients and regression analysis, the authors found that the GDP decoupled from EC in the first period. The increase in GDP led to an increase in the consumption of electricity and the electricity produced from RESs in the second period. In Q3 2020, the real GDP is different from the calculated GDP, due to the pandemic. In Romania, the electricity consumption decreased within the first nine months of the pandemic due to the economic contraction. The electricity that comes from coal and hydropower plants suffered the biggest decrease. If the electricity that comes from NRESs can be adapted to the economic demands, the quantity of electricity that comes from RESs will be influenced by the climate conditions.

Study of Energy Efficiency Upgradation and Investment Risk

It was about 60 years ago when electricity was first introduced in Bhutan and today, more than 99.97 percent of households are electrified. Since electricity is superior to other form of energy, everybody prefers it. The electricity demand is increasing rapidly with development in all fronts. Distribution system are currently experiencing rapid changes in domestic load growth. With increasing domestic electricity consumption, Royal Government of Bhutan (RGoB) recognizes the importance of energy management and reduction at the consumer end to further minimize carbon emission and to increase the exports. While achieving 100 percent electrification of Bhutan, the increased consumer reduces the export, reducing the total revenue generated. The study identifies possible barriers of energy efficiency in domestic and industrial sectors. The lack of policy framework is one of the main reasons for the decline in interest in energy efficiency. The government subsidy is another cause of the same. This in turn led to negligence of Energy Efficiency (EE) programs by the consumers. On the contrary, consumers feel that the cost of energy is high, despite the fact that urban dwellers' expenditure capacity and productivity upgradation potential are also high. Based on a case study of residential and commercial buildings in the Phuntsholing region, as well as industrial sectors in Pasakha, this paper examines the metrics needed to make energy efficiency upgrade decisions. The study finds respondents were more likely to choose equipment that suited their budget rather than those that are energy efficient. Another big impediment to investment in energy efficiency in the region is a lack of energy efficiency awareness and information.

Macroeconomic, demographic and climatic indicators for household electricity consumption model in Cyprus

ABSTRACT By 2030 Cyprus committed to reduce greenhouse gas emissions by 40% and increase renewables’ energy share by 19% according to the European engagements. Difficulties appear due to the continuous increase in domestic energy consumption, the large dependency on fossil fuels and the adverse climate. A number of macroeconomic, demographic and climatic indicators that influence Cyprus’s electricity consumption (EC) was analysed for years 2000–2018 using augmented Dickey–Fuller unit root test and the autoregressive distributed lag model (ARDL). The ARDL model revealed that an increase in population could radically increase EC in the long-run and short-run. Results posit that a 1% increase in urban population, electricity price and unemployment decrease domestic EC in the long-run by 17.25%, 0.48% and 0.30%, respectively. In the short-run smaller elasticities were found because time was too short for adjustments to be made. However, their decrease could not outweigh the population growth effect on increasing domestic EC.

An empirical investigation of domestic energy data visualizations

Which device in your home uses the most electricity? Many people have a poor understanding of their domestic energy consumption. In this paper, we evaluated three data visualizations used to deliver feedback. These were: (1) an aggregated line graph – showing changes in total electricity consumption over time, (2) a disaggregated line graph – showing changes in electricity consumed over time but separated out at the appliance-level, and (3) an area-based visualization – showing the cumulative energy consumed by different appliances over a given time period. In an experiment, 65 participants used one of these three visualizations to make sense of the same pattern of domestic electricity data. Participants who used the area-based visualization gained a more accurate understanding of how much electricity different domestic appliances were using compared to participants who were shown time series data. These results suggest that the choice of data visualization will impact people's understanding from smart metering systems, and that appliance-wise disaggregation offers the most promising approach for visualizing domestic electricity consumption data.

The Impact of the COVID-19 Pandemic on Electricity Consumption and Economic Growth in Romania

This paper analyzes the impact of the COVID-19 pandemic on economic growth and electricity consumption and investigates the hypothesis of the influence of this consumption on the gross domestic product (GDP) for Romania. Using time series on monthly electricity consumption and quarterly GDP and a multi-linear regression model, we performed an analysis of the evolution of these indicators for 2007–2020, a comparison between their behavior during the financial crisis vs. COVID-19 crisis, and empirically explore the relationships between GDP and electricity consumption or some of its components. The results of the analysis confirm that the shock of declining activity due to the COVID-19 pandemic had a severe negative impact on electric energy consumption and GDP in the first half of 2020, followed by a slight recovery. By using a linear regression model, long-term relationships between GDP and domestic and non-household electricity consumptions were found. The empirically estimated elasticity coefficients confirm the more important impact of non-household electricity consumption on GDP compared to the one of domestic electricity consumption. In the context of the COVID-19 pandemic, the results of the study could be useful for optimizing energy and economic growth policies at the national and European levels.

Estimation of domestic urban electricity consumption: A case study of Baghdad, Iraq

Electricity consumption for household purposes in urban areas widely affects the general urban consumption compared to other commercial and industrial uses, as household electricity consumption is affected by many factors related to the physical aspects of the residential area such as temperature, housing unit area, and coverage ratio, as well as social and economic factors such as family size and income, to reach the extent of the influence of each of the above factors on the amount of electricity consumed for residential uses, a selected sample of a residential area in the city of Baghdad was studied and a field survey conducted of the characteristics of that sample and the results analyzed and modeled statistically in relation to the amount of electricity consumed for residential units. Results showed that the annual electricity consumption is directly proportional to both the increase in the residential plot area and the increase in the number of residents of the housing unit and the increase in the monthly income of households, while it is inversely proportional to the plot coverage rate. Through the results of the field research, it was possible to build six quantitative models describing the behavior of electricity consumption in relation to the variables covered in the research.

Estimation of domestic urban electricity consumption: A case study of Baghdad, Iraq

Electricity consumption for household purposes in urban areas widely affects the general urban consumption compared to other commercial and industrial uses, as household electricity consumption is affected by many factors related to the physical aspects of the residential area such as temperature, housing unit area, and coverage ratio, as well as social and economic factors such as family size and income, to reach the extent of the influence of each of the above factors on the amount of electricity consumed for residential uses, a selected sample of a residential area in the city of Baghdad was studied and a field survey conducted of the characteristics of that sample and the results analyzed and modeled statistically in relation to the amount of electricity consumed for residential units. Results showed that the annual electricity consumption is directly proportional to both the increase in the residential plot area and the increase in the number of residents of the housing unit and the increase in the monthly income of households, while it is inversely proportional to the plot coverage rate. Through the results of the field research, it was possible to build six quantitative models describing the behavior of electricity consumption in relation to the variables covered in the research.

On the spatiotemporal complementarity of the European onshore wind resource

Among the renewable energy sources, the highest share of European net electricity generation comes from wind power. However, the European onshore wind resource’s volatile nature is a significant challenge in ensuring a constant national electricity supply. Therefore, this study examined the potential of complementary use of national wind resources available in 33 European countries. The complementarity of the national wind resources was assessed for 1971–2010 by identifying the time scales explaining the largest part of the variance in the time series of daily wind energy yield. The results of a novel combination of wavelet analysis, graph models, and dynamic time warping indicate that the wind energy yield shows the most substantial variations at the annual, seasonal, and multi-day scales. Geographical proximity is a critical factor in the complementarity of national wind energy yields. At all evaluated time scales, the wind energy yield in most central and western European countries is strongly correlated, forming a large supranational network with low potential for complementary use of national wind resources. Although the seasonal component of daily wind energy yield is shifted between the European countries by up to several weeks, which would create potential for complementary use, domestic electricity consumption often exceeds the usable wind resources. Domestic electricity consumption was found to be a major barrier to the transboundary exchange of wind energy. Based on the results obtained for the European onshore wind resource’s spatiotemporal dynamics, it must be assumed that the potential for regular complementary transboundary use of wind resources is limited.

On the real performance of groundwater heat pumps: Experimental evidence from a residential district

• Heat pump is a key clean energy technology for residential buildings. • Monitoring and data analysis is key to optimize system integration and operation. • Real performance of groundwater multipurpose heat pumps is lower than expected. • Improvements to the heat pumps design and control logic are proposed and validated. • Systematic approach for system data collection and analysis is needed. Heat pumps are among the most promising technologies driving energy transitions towards the carbon reduction objectives. Their use is being particularly encouraged in the residential sector, where the heating and cooling uses account for the larger energy demand. However, their real performance often does not correspond to designers’ expectations, leading to higher running energy costs than expected, and the reasons should be primarily investigated by means of large data collection and analysis. This paper reports the results of a long-term monitoring campaign of a multipurpose groundwater heat pump system for a district of multifamily buildings in Italy. The survey includes data on thermal energy consumption for space heating, domestic hot water, space cooling and electricity consumption of the groundwater heat pumps taken between 2017 and 2018. It was shown that the performance of the installed groundwater heat pumps was significantly lower (up to 60%) than expected. Some defined strategies, mainly related to the system control logic, were demonstrated to be able to significantly increase the system performance, highlighting the need for improvements in integrated design and control strategies since the early design phase and for continuous monitoring in operation to optimise the system integration in real contexts.

A MILP optimization method for energy renovation of residential urban areas: Towards Zero Energy Districts

The nearly Zero Energy Building concept should be adopted in the renovation of existing buildings and actions upscaled and applied to larger and larger urban areas. In this context, this paper presents an optimization methodology for drafting integral renovation actions at district level. The methodology is based on a MILP model that allows selecting energy saving measures, as well as the design and operation of energy supply systems amongst a very large set of potential permutations. For its application, the concept of District Equivalent Building is introduced, defined as a virtual building with a set of energy loads equal to the addition of all the individual load vectors of each building.The methodology was applied to a real residential district located in Bilbao (northern Spain). For this case study, the optimal renovation plan was obtained, considering for that three different optimization objectives: (i) Optimal-Cost, (ii) Zero Energy District (ZED) and (iii) Zero Energy District including domestic electricity consumption in the calculation of the non-renewable primary energy consumption (ZED’). Although similar solutions were adopted for each of these cases, significant differences were get attending the hourly operation of the solutions. These cases were economically assessed and the optimal-cost curve was obtained.

Domestic Electricity Consumption in Mexican Metropolitan Areas under Climate Change Scenarios

The following estimates analyse human bioclimatic conditions due to climate change in three time horizons, as suggested by Article 2 of the Paris Agreement. Each scenario corresponds to an increase in the global average temperature (∆T) of 1 ºC, 1.5 ºC and 2 ºC, respectively. The measurements of residential electricity consumption for air conditioning were made in 30 metropolitan areas of Mexico with at least half a million inhabitants in 2010. Bioclimatic conditions also included estimates of the effects of urban heat islands (UHI). Use of heating will decrease and, in some cases, disappear, while the need for cooling will increase. Electricity consumption due to cooling is expected to increase in Mexicali, Reynosa-Río Bravo (on the border with the United States), Cancún, Villahermosa, and Veracruz (on the shores of the Caribbean Sea and the Gulf of Mexico). Urban areas like Toluca, Pachuca, Xalapa, San Luis Potosí, and Puebla-Tlaxcala used little or no energy for cooling in the second decade of the 21st century but will need to do so halfway through the century.