Aggregate Energy Consumption Research Articles

On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters

Energy smart meters have become very popular in monitoring and smart energy management applications. However, the acquired measurements except the energy consumption information may also carry information about the residents’ daily routine, preferences and profile. In this article, we investigate the potential of extracting information from smart meters related to residents’ security- and privacy-sensitive information. Specifically, using methodologies for load demand prediction, non-intrusive load monitoring and elastic matching, evaluation of extraction of information related to house occupancy, multimedia watching detection, socioeconomic and health profiling of residents was performed. The evaluation results showed that the aggregated energy consumption signals contain information related to residents’ privacy and security, which can be extracted from the smart meter measurements.

A Data-Driven Method for Unsupervised Electricity Consumption Characterisation at the District Level and Beyond

A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper's novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterization of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector.

INFORMATION COMMUNICATION TECHNOLOGY ACCESS AND USE TOWARDS ENERGY CONSUMPTION IN SELECTED SUB SAHARAN AFRICA

Major new opportunities abound from energy integration among regions in Africa with the sole aim of reducing transaction costs and with the role of Information Communication Technology (ICT), it would take energy from where it is easily affordable to places where it is needed. Given the hullabaloo over the net energy-saving impact of ICT, this study takes on a new perspective, that is, employing household energy consumption, to ascertain the use of ICT by household in accessing energy. The study reconnoiters the degree to which Energy integration among the five regional power pools in Africa can achieve ending energy poverty among regional adherents by means of three measures of energy sustainability, explicitly: energy security, energy equity and environmental sustainability . The study utilizes the Pooled Ordinary Least Squares technique on data from the SSA economies over the period 2000-2019. The study expects the results to help in suggesting the need for the acceleration of ICT development in Africa (Sub-Saharan) nations, given the universal communal mission of sustainable energy consumption. Keywords: Technology and Distribution, Common Knowledge, Aggregate Energy Consumption, Sustainable Development, Green Technology, ICT-based JEL Classifications: O330, D8, Q430, QO10, Q4, Q550, O300, D13 DOI: https://doi.org/10.32479/ijeep.9990

Analysis of factors affecting energy consumption and CO2 emissions in Thailand's road passenger transport

The transport sector is one of the important contributors of increasing energy consumption and CO2 emissions in Thailand. Due to rapid development of transport infrastructure and technologies, patterns of energy consumption in this sector, as well as emissions, have changed considerably. To understand changes of aggregate energy consumption and CO2 emissions in this sector, this study employs the decomposition technique of the additive LMDI-I index method to analyze influencing factors in the road passenger transport in Thailand during 2007–2017. Results indicate that major energy consumption and GHG emissions in Thailand's road passenger transport come from sedans, vans, and taxis. The decreasing GHG emissions from fuel share and emission factors revealed the success of biofuel promotion in the road transport. The policy implication on energy efficiency and CO2 mitigation suggests that Thailand should continue promotion of energy efficiency improvement, public transport, biofuels and electric vehicles.

The moderating role of renewable and non-renewable energy in environment-income nexus for ASEAN countries: Evidence from Method of Moments Quantile Regression

A vast body of studies estimates the impact of energy consumption on the environment. A typical empirical study either use aggregate energy consumption or apply conventional econometric techniques in modelling the nexus of energy, income and environment. To correct these gaps, the objective of the study is to use renewable and non-renewable energy consumption in analyzing energy-income-environment nexus, and to apply the novel Method of Moments Quantile Regression for ASEAN countries. The outcomes indicate that non-renewable energy consumption stimulate carbon emissions across all quantiles (10th to 90th), the value of the 10th quantile is 0.257 which rises to 0.501 till 90th quantile. Whereas, the renewable energy consumption leads to a decrease in CO2 emissions across all the quantiles (10th to 90th) but this association is statistically insignificant at higher quantiles from 60th to 90th. The empirical outcomes also verify the presence of the environmental Kuznets curve relationship, which is statistically significant from the middle (30th) to higher (90th) quantiles. Moreover, the finding of panel estimation approaches (FMOLS, DOLS, FE-OLS) also verify the existence of the EKC hypothesis in ASEAN countries. Their finding also describes that 1% increase in non-renewable energy consumption increase CO2 emission by 0.29%, 0.26% and 0.30% whereas 1% increase in the usage of renewable energy reduces CO2 emission by 0.17%, 0.15% and 0.17% in case of FMOLS, DOLS and FE-OLS respectively. The empirical results conclude that the government should encourage and subsidize the sources of green energy to tackle environmental degradation. More policy implications are further discussed in the study.

Does ICT trade facilitate renewable energy transition and environmental sustainability? Evidence from Bangladesh, India, Pakistan, Sri Lanka, Nepal and Maldives

Consumption of fossil fuels has triggered the worldwide awareness to attain socioeconomic and environmental sustainability, particularly by enhancing renewable energy use and mitigating the environmental adversities, in tandem. Against this background, this paper aimed to investigate the impacts of promoting ICT trade, through liberalization of the associated trade barriers, on the prospects of undergoing renewable energy transition and limiting environmental degradation by curbing CO2 emissions across six South Asian economies. The overall results from the econometric analyses, in a nutshell, confirm that higher degrees of openness to ICT trade lead to greater consumption of renewable energy, improve energy use efficiency levels and enhance access to cleaner cooking fuels. However, ICT trade fails to elevate the renewable energy shares in aggregate final energy consumption figures in South Asia. Besides, ICT trade is also seen to boost CO2 emissions across this region; although the impacts seem to reverse upon enhancement in renewable energy consumption levels along with liberalization of the ICT trade barriers. Thus, these results impose key policy implications for the South Asian governments for simultaneously ensuring energy security and sustaining environmental well-being across South Asia.

Energy consumption and economic growth in Botswana: empirical evidence from disaggregated data

ABSTRACT This paper examines the relationship between energy consumption and economic growth in Botswana during the period 1980–2016. We disaggregate energy consumption into six components, namely: total energy consumption, electricity consumption, motor gasoline, gas/diesel oil, fuel oil and liquefied petroleum gas. We then compare the results of the disaggregated energy components with that of the aggregated energy consumption level. In order to account for the omission-of-variable bias, we incorporate inflation and trade openness as intermittent variables between the various components of energy consumption and economic growth, thereby creating a system of multivariate equations. Using the ARDL-bound testing approach, the study found a causal flow from economic growth to energy consumption to predominate. This finding has important policy implications as it shows that the buoyant economic growth that Botswana has enjoyed over the years is not energy-dependent, and that the country could pursue the requisite energy conservation policies without necessarily stifling its economic growth. To our knowledge, this study may be the first of its kind to examine in detail the causal relationship between energy consumption and economic growth in Botswana using a multivariate causality model and a disaggregated dataset.

Persistence in per capita energy consumption: A fractional integration approach with a Fourier function

Abstract In this study, we investigate the degree of persistence for aggregate energy consumption per capita for 113 countries over the period 1960–2014 through a recently introduced fractional unit root test with a Fourier function to allow multiple smooth structural breaks. We also employ the ADF unit root test, the efficient Wald test for fractional unit-roots, and the Fourier ADF test to make a comparison and to enrich the study. The empirical results considering the fractional Fourier unit root test indicate two crucial points. First, shocks have only temporary effects on energy consumption per capita, and it will vanish slowly as a result of the long memory characteristic. Therefore even transitory shocks have persistence effects, which require permanent policies due to their nature. This inference is different from what would be erroneously suggested by the dichotomous approach for stationarity and nonstationarity that temporary shocks will have transitory effects. Second, the integration degree of energy consumption per capita may change due to the existence of a structural break; therefore, policymakers must be aware of the varying integration degree to design and implement the best policies.

Sectoral convergence in energy consumption from developing country perspective: The case of Turkey

Recent studies investigating convergence in energy consumption at the sectoral level within a country suggest that aggregate energy consumption could mask considerable differential impacts that might be observed at the sectoral level. This study aims to contribute and complement the existing convergence literature with an attempt to analyze sectoral convergence in energy consumption from the developing country perspective. To this end, we choose Turkey as a developing country and employ both the conventional unit root tests and the residual augmented least squares-Lagrange multiplier (RALS-LM) methodology to investigate stochastic conditional convergence in per capita energy consumption at the sectoral level. Our findings suggest some interesting outcomes that could be relevant from the sectoral energy consumption perspectives for developing countries. While energy consumption in Turkey shows a trending upward, the leading sectors of industry and transport energy consumption per capita diverge from the mean consumption and, therefore, lead to further increases in energy consumption and energy-related emissions. Moreover, agriculture and the other sector, whose consumption per capita values are below the mean, converge towards the mean. Overall, Turkish sectoral energy consumption trends follow developing countries’ patterns, and the empirical findings suggest that this trend is worrying from the sustainability perspective.

Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids

This paper presents a state estimation approach to address the problem of identifying the phase to which single-phase customers are connected in three-phase distribution grids. The proposed method performs Kalman filtering on the information provided simultaneously by the smart meter of every customer and the aggregated energy consumption measured at each phase of the secondary substation feeding the set of customers. Different nonlinear formulations of the Kalman filter are tested and their performance compared, showing that the ensemble Kalman filter provides better estimation results when the system size increases. The accuracy, robustness and limitations of the estimator are also tested when measurement errors are considered.

Modeling natural gas consumption, capital formation, globalization, CO2 emissions and economic growth nexus in Malaysia: Fresh evidence from combined cointegration and causality analysis

The discovery of natural gas in the 20th century has increased aggregate energy consumption while spurring economic development. However, very little attention has been given in the energy economics literature, especially in Malaysia. As such, this paper primarily revisited the natural gas — economic growth nexus hypothesis in the case of Malaysia. The study was conducted with data from 1980 to 2014 in a multivariate framework with the inclusion of capital formation, globalization, and CO2 emissions to avoid omitted variable bias. We investigated the stationarity properties with a method that accommodates a single structural break. Subsequently, the novel combined co-integration test in conjunction with several techniques were used to assess the magnitude of the long-run equilibrium relationship. The empirical findings trace the long-run equilibrium relationship among the variables over the sampled period. The Granger causality test analysis confirmed the growth-energy driven hypothesis in Malaysia. The findings call for the adoption of cleaner and environmentally friendly energy sources in the Malaysian energy mix. We highlight the need for pragmatic strides from both private and public energy sector stakeholders to prioritize clean and accessible energy in line with the Sustainable Development Goals.

Surfing the microwave oven learning curve

The microwave oven has penetrated into many kitchens throughout the world and has substituted old polluting forms of cooking with new cleaner ways. Its exponential market uptake in the residential sector has been realized thanks to substantial cost reductions during its deployment phase, which started more than 70 years ago. Today, the annual production capacity reaches almost 100 million units per year. The cumulative number of microwave ovens produced since their commercial launch in 1947 surpasses 1.7 billion units to date. We express the observed price reduction during the expansion of the microwave oven market by a learning curve, which reveals a learning rate of 20 ± 2%. We project that at least two doublings in cumulative capacity are probably still to come over the next few decades, thanks to both replacements and further market uptake. This additional scale-up could imply close to another 40% reduction in the price of microwave ovens. Despite today’s total installed capacity of around 1.1 TW, the aggregate energy consumption of all microwave ovens in use is low as a result of their limited usage time. This may change in the future if novel applications of microwave technology are implemented, for instance for heating or conversion purposes. A significant increase in energy consumption is likely to be expected if, for example, the technology diffuses in industry or finds its way in the energy sector, for instance as pyrolysis or plasma-generating device. We recommend that innovative microwave technology options are explored in more detail, and that their potential role in the energy transition is further investigated.

Does urbanization lead to less residential energy consumption? A comparative study of 136 countries

The impact of urbanization on residential energy consumption is insufficiently understood. By employing a panel dataset of 136 countries during 1990–2015, this study examines the influence of urbanization on residential energy use worldwide, and how the magnitudes of influence vary across regions by taking into account regional heterogeneity and stages of urbanization. Our findings indicate that the impact of urbanization on residential energy consumption tends to vary greatly across regions in different urbanization stages. Most countries in Sub-Saharan Africa are at the acceleration stage of urbanization characterized by rapid population migration to urban areas without the corresponding economic growth. This feature of urbanization leads to a decrease in aggregate consumption. In contrast, the urbanization in developing regions in Asia and the Middle East & North Africa, which has been accompanied by booming economies, can increase aggregate residential energy consumption. Surprisingly, for highly urbanized regions, including the developed regions of the world, Latin America & the Caribbean, and the developing regions in Europe & Central Asia, the urbanzation impact seemingly becomes nonsignificant because of the narrow gap between urban and rural energy use in the residential sector. Based on these findings, we make four key policy suggections, including evaluating fiscal sustainability of household energy transition programs on a cost-benefit basis, developing a suite of customized options for household energy use and orderly transition process, encouraging bottom-up initiatives for adopting clean energy in residential sector, and sharing a public monitoring and planning platform.

Non-intrusive energy disaggregation by detecting similarities in consumption patterns


 
 
 Breaking down the aggregated energy consumption into a detailed consumption per appliance is a crucial tool for energy efficiency in residential buildings. Non-intrusive load monitoring allows implementing this strategy using just a smart energy meter without installing extra hardware. The obtained information is critical to provide an accurate characterization of energy consumption in order to avoid an overload of the electric system, and also to elaborate special tariffs to reduce the electricity cost for users. This article presents an approach for energy consumption disaggregation in households, based on detecting similar consumption patterns from previously recorded labelled datasets. The experimental evaluation of the proposed method is performed over four different problem instances that model real household scenarios using data from an energy consumption repository. Experimental results are compared with twobuilt-in algorithms provided by the nilmtk framework (combinatorial optimization and factorial hidden Markov model). The proposed algorithm was able to achieve accurate results regarding standard prediction metrics. The accuracy was not affected in a significant manner by the presence of ambiguity between the energy consumption of different appliances or by the difference of consumption between training and test appliances.
 
 

Characterization of cooling loads in the wine industry and novel seasonal indicator for reliable assessment of energy saving through retrofit of chillers

The food sector is a major consumer of energy and growing efforts are being made in the search for solutions that will guarantee the efficient and sustainable use of energy resources. Among the different sectors, wineries are attracting particular interest due to the continuous growth of the global market and production. Surveys conducted in the winemaking sector have highlighted the importance of performing accurate energy audits and have identified the installation of efficient refrigeration systems as a promising solution in a variety of cases. Unfortunately, the savings achievable by efficient cooling technologies are often estimated using simplified approaches which do not take into consideration the actual operating conditions of the equipment typically variable on seasonal and daily bases. In this paper a novel bottom-up procedure is presented, aimed at developing reliable profiles for refrigeration and air-conditioning loads and at assessing the extent to which more efficient chilling units could contribute to reducing electricity consumption. The use of standard Seasonal Energy Efficiency Ratios is critically discussed and a novel customized indicator is proposed. The method is applied to a medium-scale winery producing still red and white wines and sparkling wines, for which only aggregated energy consumption data are available. After deriving detailed load profiles, it is proven that the use of standard seasonal indicators leads to 56.85% and 83.87% overestimation of potential energy savings, respectively, for low and medium temperature cooling energy uses, confirming the importance of adopting seasonal indicators customized on the actual operating conditions of chillers.

The Energy Consumption of Radiology: Energy- and Cost-saving Opportunities for CT and MRI Operation.

Background Awareness of energy efficiency has been rising in the industrial and residential sectors but only recently in the health care sector. Purpose To measure the energy consumption of modern CT and MRI scanners in a university hospital radiology department and to estimate energy- and cost-saving potential during clinical operation. Materials and Methods Three CT scanners, four MRI scanners, and cooling systems were equipped with kilowatt-hour energy measurement sensors (2-Hz sampling rate). Energy measurements, the scanners' log files, and the radiology information system from the entire year 2015 were analyzed and segmented into scan modes, as follows: net scan (actual imaging), active (room time), idle, and system-on and system-off states (no standby mode was available). Per-examination and peak energy consumption were calculated. Results The aggregated energy consumption imaging 40 276 patients amounted to 614 825 kWh, dedicated cooling systems to 492 624 kWh, representing 44.5% of the combined consumption of 1 107 450 kWh (at a cost of U.S. $199 341). This is equivalent to the usage in a town of 852 people and constituted 4.0% of the total yearly energy consumption at the authors' hospital. Mean consumption per CT examination over 1 year was 1.2 kWh, with a mean energy cost (±standard deviation) of $0.22 ± 0.13. The total energy consumption of one CT scanner for 1 year was 26 226 kWh ($4721 in energy cost). The net consumption per CT examination over 1 year was 3580 kWh, which is comparable to the usage of a two-person household in Switzerland; however, idle state consumption was fourfold that of net consumption (14 289 kWh). Mean MRI consumption over 1 year was 19.9 kWh per examination, with a mean energy cost of $3.57 ± 0.96. The mean consumption for a year in the system-on state was 82 174 kWh per MRI examination and 134 037 kWh for total consumption, for an energy cost of $24 127. Conclusion CT and MRI energy consumption is substantial. Considerable energy- and cost-saving potential is present during nonproductive idle and system-off modes, and this realization could decrease total cost of ownership while increasing energy efficiency. © RSNA, 2020.

A sleep‐and‐wake technique for reducing energy consumption in BitTorrent networks

SummaryFile sharing is one of the leading Internet applications of P2P technology. Given the high number of computer nodes involved in peer‐to‐peer networks, reducing their aggregate energy consumption is an important challenge to be faced. In this paper, we show how the sleep‐and‐wake energy saving approach can be exploited to reduce energy consumption in BitTorrent, one of the most popular file sharing peer‐to‐peer networks. We describe BitTorrentSW, a sleep‐and‐wake approach for BitTorrent networks that allows seeders (ie, peers that hold complete files) to cyclically switch between wake and sleep modes to save energy while ensuring good file sharing performance. The decision to switch to sleep mode is taken independently by each seeder based on local information about the composition of the peer‐to‐peer network. BitTorrentSW has been evaluated through PeerSim using real BitTorrent traces. The simulation results show that, in all the configurations under analysis, the percentage of energy saved by BitTorrentSW is much higher than the percentage of increase in download time. For instance, in a network with 50% of seeders, about 20% of energy is saved using BitTorrentSW, with an increase of only 7% of the average time needed to complete a file download compared to a standard BitTorrent network in which all seeders are always powered on.

A Cooperative Green Content Caching Technique for Next Generation Communication Networks

Proactive content caching at the network-edge is envisioned as a key technique to reduce backhaul congestion and latency in popular content delivery. Data storage units, however, require additional energy, which offers a challenge to researchers who intend to reduce energy consumption up to 90% in next generation networks. The concept of powering data storage units by renewable energy aligns with this energy reduction target. In this article, we introduce a proactive caching strategy, which caches content in helper nodes in a cooperative manner considering intermittent renewable energy and variable content load for various hours of the day. The hypothesis is that the amount of renewable energy available to various helper nodes varies depending on the node location and time of the day. The content load across helper nodes also varies with time. As such, if helper nodes work cooperatively by considering the available renewable energy and content load at each helper node, the aggregate non-renewable energy consumption for content caching can be further reduced. We model the research challenge as an optimization problem. Furthermore, we also present a heuristic solution. Our proposed scheme achieves a significant reduction in non-renewable energy consumption by 23% over the existing green solution.

Capital - Energy Substitution: Does Energy Sources Matter for the Elasticity of Substitution? An Empirical Investigation for OECD Countries

Energy and other factors of production are the primary inputs in the context of sustainable economic growth. This makes energy relatively strategic in the development stage and in the energy importers economies. In this study, the effect of elasticity of substitution on economic growth is investigated for 31 OECD countries over the period from 1990-2014. Although the effect of elasticity of substitution between capital and aggregate energy consumption on economic growth has been examined in many studies, the issue of how this effect changes between the different sources of energy has not been adequately addressed. The research is aimed at contributing to the related literature in this regard. The effect of elasticity of substitution between capital and different energy sources on economic growth is examined within the neoclassical Solow-Swan Growth model. In this model, technology is defined by a variable elasticity of substitution (VES) production function. The empirical model is estimated by the non-linear least squares (NLLS) method. The results show that oil and primary energy consumption are substitutes; natural gas, coal, and hydroelectricity consumption are the complement in the final production of output.

Cross-Layer Design and Analysis of Wireless Geophone Networks Utilizing TV White Space

Traditional seismic data acquisition systems used for surveying during the exploration of oil and gas rely on cables between geophones and the data collection center. Despite the fact that cable-based systems provide reliable seismic data transfer, their deployment and maintenance costs increase substantially as the survey area increases in scale. Therefore, a three layer wireless network architecture is proposed in this work, which consists of wireless geophones (WG) and a data center with an intermediate wireless gateway node (WGN). This paper investigates the aggregate data throughput, transmission time, and energy consumption from WGs to the WGN in a wireless geophone network architecture based on the IEEE 802.11af standard. This standard is considered in order to have the maximum possible range and low power consumption due to operating in TV bands. Analytical expressions of the aforementioned quantities are derived using Markov chain models. Two Markov models are considered for this purpose: one for modeling the access method that allows multiple WGs to connect to a WGN and the other for representing a buffer in a WG. Since seismic data is recorded at regular intervals, arrivals of data packets in the buffer of the WG is deterministic. On the other hand, departure is random due to the multiple access method. Hence, in this work D/M/1/B queue is used for the first time to model the buffer in a wireless geophone. Furthermore, the physical layer constraints are also taken into account together with proper wireless path-loss channel models. The results obtained are useful for designing such wireless seismic networks without extensive simulations. In particular, the proposed joint medium access control, physical layer, and D/M/1/B model enables us to optimize the required number of WGNs. Finally, sectoring is also introduced in order to minimize the total number of WGNs needed to cover the whole surveying area.