Aggregate Electricity Consumption Research Articles

Modelling and projecting regional electricity demand for Saudi Arabia

AbstractWe address the shortcomings associated with modelling national aggregate electricity consumption in a large and diverse country with unique regional characteristics. Focusing on Saudi Arabia, we employ an econometric approach to analyse the distinct responses of its regions to electricity prices and income levels. The study employs a general to specific modelling approach, utilizing data from 1990 to 2019 within the conventional energy demand theoretical framework. Our region‐specific estimations reveal income and price as the primary drivers, with the southern region exhibiting a higher sensitivity to income, while the eastern region shows a comparatively smaller response to prices attributed to its significant industrial presence. Elasticities for the central and western regions align with previous research, while eastern and southern regions exhibit larger elasticities. Weather impacts are observed only in the warmest western region, characterised by a substantial share of residential electricity consumption. Furthermore, we utilise our estimated model to project a regional baseline demand for electricity in Saudi Arabia and demonstrate how prices affect regions differently. This information is important for an oil‐exporting country like Saudi Arabia, considering the diverse fuel mix used for electricity generation across regions. Assuming moderate economic growth and no price change, our baseline projections indicate a total electricity demand of 366 terawatt hours (TWh) by 2030.

An Overview of Electricity Consumption in Europe: Models for Prediction of the Electricity Usage for Heating and Cooling

Although aggregate electricity consumption provides valuable information for market analysis, demand composition, including industrial, residential, illumination, and other uses, and special days, such as national or religious holidays and annual industrial shutdowns, differ for each country. This paper analyzes the hourly electricity consumption of European countries in the European Transmission System Operation for Electricity (ENTSO-E) grid from 2006 to 2018. We propose an outlier detection method to identify special days and a modulated Fourier Series Expansion model to determine the breakdown of industrial versus household consumption and heating versus cooling consumption. The proposed outlier detection method uses the time series for each hour and checks whether a day has more than a threshold number of hours with exceptional electricity consumption levels. The proposed demand prediction model has a 3% average error when electricity usage for heating is not dominant. It also allows country classification based on consumption patterns to efficiently manage regional or country-based electricity markets.

A machine learning framework to assess the impact of the COVID-19 pandemic on electricity consumption patterns

Energy self-sufficiency and resilience are essential elements for long-term urban sustainability. Energy supply in many cities in the United States has been highly dependent on fossil fuels and excessive energy consumption, both at home and outside (i.e., residential and non-residential energy consumption). In this work, we identify electricity consumption spatial trends and profiles across neighborhoods in the city of Chicago using a self-organizing map (SOM) machine learning method as a main tool. Toward this goal, we use an anonymous dataset provided by Commonwealth Edison (ComEd; Chicago's main electricity provider), which includes electricity consumption data of individual residential and non-residential accounts in Chicago. We aggregate electricity consumption to the zip codes level, apply machine learning clustering methods to categorize electricity consumption patterns, and finally couple it with land use variables to find spatial interrelations. The output demonstrates how specific profiles of electricity consumption emerge across zip codes, which is the first step for developing tailored policies to lower energy consumption. The result of this study can help engineers, urban planners, and policymakers develop actionable strategies for electricity consumption management.

Leveraging sequence-to-sequence learning for online non-intrusive load monitoring in edge device

Non-intrusive load monitoring (NILM), extracting the appliances’ usage profiles by decomposing a household’s aggregate electricity consumption, has become increasingly important for home energy management. Studies show that users are more likely to adjust their usage behaviors if the real-time feedback on their energy consumption details is provided. Recent state-of-the-art deep learning models have delivered a good performance on load disaggregation; however, these models require tremendous computation and memory to tune huge numbers of parameters, which makes them difficult to be implemented on edge devices to derive real-time appliance usage information for users while keep user’s privacy. In this paper, a lightweight sequence-to-sequence neural network model consisting of only two convolutional layers and dense layers is proposed, which can provide users with both real-time appliance power usage and improved energy consumption profiles. The online results are produced by picking up every end-point of output sequences of the network, whereas the past energy profiles or offline results of appliance can be obtained by averaging all points of the output sequence. By implementing the model in edge device, the user’s privacy is maintained. The lightweight NILM algorithm is evaluated with low-frequency sampling data in two public available datasets: UKDALE and REFIT. Assessment results prove that the proposed model exhibits comparable or even better disaggregation performance with dramatically reduced model size and computation requirements, and thus demonstrates its application potential on edge devices.

Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid

As the next-generation grid, the smart grid (SG) can significantly enhance the reliability, flexibility as well as efficiency of electricity services. To address latency and bandwidth issues during data analysis, there have been attempts to introduce fog computing (FC) in SG. However, fog computing-based smart grid (FCSG) face serious challenges in security and privacy. In this paper, we propose a privacy-preserving data aggregation scheme that supports dynamic billing and arbitration, named PPDB. Specifically, we design a four-layer data aggregation framework which uses fog nodes (FNs) to collect and aggregate electricity consumption data encrypted under the ElGamal cryptosystem and employ distributed decryption to achieve fine-grained access and bills generation based on real-time prices. In addition, we introduce a trusted third party to arbitrate disputed bills. Detailed security analysis proves that the proposed PPDB can guarantee the confidentiality, authentication and integrity of data. Compared with related schemes, the experimental results show that the communication overhead of our scheme is reduced by at least 38%, and the computational efficiency in the billing phase is improved by at least 40 times.

Base Station Energy Use in Dense Urban and Suburban Areas

Growing energy consumption is a global problem. The information and communications technology (ICT) industry is in a critical role as an enabler of energy savings in other sectors. However, the power consumption of the ICT sector also needs to be addressed, to contribute to the overall reduction of power consumption and carbon emissions. A new era has begun as the fifth generation (5G) mobile data connection rollouts are advancing globally and are expected to reach a 10% share of end-user devices and connections by 2023. The available references on energy consumption in global mobile networks are rather old and highly averaged – only estimates of energy consumption relative to data volumes are available. There is an information gap regarding the energy consumption of emerging 5G and advanced 4G technologies. Therefore, it has been difficult to understand the actual electricity consumption differences between generations and spatially aggregated electricity consumption once these generations are combined to offer capacity and coverage. This article fills this gap by providing a reference on the energy consumption of base transceiver stations for reported mobile data usage for different Radio Access Technologies; 3G, 4G and 5G respectively. To the best of our knowledge, there is no reference to scientific research on the comparison of energy intensity per square kilometer for 3G, 4G and 5G mobile radio technologies, using actual operator data. The objective of this research was to improve the understanding of the actual energy consumption of different Radio Access Technologies (RAT). The results also give insight to decision makers on when to modernize the operator radio access network. The article reports on the results of field measurements on data and visitor volumes and shares of different RATs. The research contains two statistical RAT combination cases, one representing the European average and the other Finnish mobile networks. The analyses were done for dense urban (DU) and suburban (SU) areas.

Flexibility characterization of residential electricity consumption: A machine learning approach

In this paper, we propose a methodology based on machine learning techniques to characterize the flexibility of electricity consumption in the residential sector. The main challenge is that the characterization of flexibility requires to know which and when appliances are being used and how available are users to change its utilization: However, this type of data is not generally available. In this work, we propose a full-stack methodology to solve this problem: we start by processing total electricity consumption data with feature engineering; then we use Non-Intrusive Load Monitoring (NILM) or Interactive Learning (IL) to identify the use of the appliances with higher flexibility (water heating, space heating and clothes drier); then we apply a Random Forest classifier to identify when the flexible appliances are being used; and finally we apply a K-means clustering algorithm to evaluate the flexibility of such appliance. We compare the results using accuracy, recall, and f-score indicators. The results show that the proposed approach can be used to characterize with high accuracy the use of flexible appliances just based on aggregated electricity consumption collected by smart meters with a low sampling rate. Further, we also demonstrate that Interactive Learning is a viable alternative approach to NILM to disaggregate electricity consumption. • Proposes the use of machine learning to estimate the flexibility of individual appliances in the residential sector. • Proposes the use of Interactive Learning to forecast the use of individual appliances. • Compares the use of Interactive Learning with a commercial Non-Intrusive Load Monitoring approach.

A Smart Cross-System Framework for Joint Allocation and Scheduling With Vehicle-to-Grid Regulation Service

The growing popularity and penetration of electric vehicles (EVs) bring in challenges and opportunities to the power grid. They represent a huge aggregate electricity consumption from the city power grid, while it is possible to make use of their batteries for supporting vehicle-to-grid (V2G) regulation service. Besides, there is a need to coordinate a collection of EVs in an intelligent manner through the smart transportation system in a smart city. In this paper, we propose a novel smart cross-system framework to support V2G regulation service in a smart city. Specifically, by considering electric buses (EBs) as a particular group of EVs with large battery packs installed and predictable operation schedules, we jointly solve the problems of EB allocation and scheduling with V2G regulation service. First of all, by using the deep learning approach, the short-term city traffic conditions can be predicted via the gated recurrent units (GRUs) neural network. With the predicted traffic conditions, we then formulate the EB allocation and V2G scheduling as a mixed-integer quadratic programming (MIQP) problem. In order to solve this problem effectively, we utilize the Lagrangian dual decomposition method to decouple the main problem into subproblems for EBs and devise a distributed algorithm to solve each subproblem. The simulation results show that our proposed approach is effective in EB allocation and V2G scheduling for the regulation service. Through efficient EB routing strategies under accurate predicted traffic conditions, the power fluctuations of the city power grid can be well flatten by providing the V2G regulation service.

Benefits of electrifying app-taxi fleet – A simulation on trip data from New Delhi

Battery electric vehicles (BEVs) appear integral to improving urban air quality, especially in less-wealthier countries whose cities rank amongst the most polluted and fastest growing. But despite steep declines in battery prices and generous subsidies, BEV adoption in such countries is weak. Here, the tremendous growth of app-taxi and retail delivery vehicles presents a massive new opportunity. Since they register several-fold greater utilization relative to the average pure private-use vehicle, electrifying such vehicles could deliver rapid financial payback and much greater economic and pollution benefits. A general framework for spatially-detailed estimation of charging infrastructure, and the economic and environmental benefits of commercial fleet electrification was developed and applied to a dataset comprising ∼ 730,000 app-taxi trips spanning ∼ 15 million kilometers (km) in Delhi, India, which shows: i) ∼ 23000 BEVs with 200 km range and a network of 3000 50 kilo-Watt chargers could satisfy 100% of Delhi’s daily app-taxi demand, and relative to a compressed natural gas (diesel) fleet eliminate 180 (7 0 0) and 0.14 (70) metric tonnes of vehicle tail-pipe NOX and particulate matter emissions respectively annually and reduce lifecycle greenhouse gas emissions by 15% (27%) per km. Cost savings are sensitive to retail BEV price and government subsidies, which are not indicative of future prices given the infancy of BEV industry. Under current prices and subsidies in India, BEVs reduce levelized cost per km 21% and 37% relative to CNG and Diesel vehicles respectively. Electrification of taxi fleets increases aggregate electricity consumption 2% with insignificant increase during daily peak demand hours. Therefore, ambitious zero emissions vehicle targets for commercial light duty vehicle fleets appear environmentally, economically and socially sound.

The relationship between site planning and electricity consumption: An empirical analysis of multi-unit residential complexes in Seoul, Korea

To cope with the exacerbating housing problem due to urban population increase and rapid urbanization, the number of multi-unit residential complexes (MURCs) is sharply rising. MURCs account for approximately 30% or more of housing stocks in the New York and Los Angeles metropolitan areas; and much higher in developing world, for example, 58% in Seoul, Korea. Likewise, the share of electricity consumption of MURCs in cities is increasing. Most existing literature analyzed the electricity consumption at household or house level and neglected the electricity consumption in publicly shared spaces and utilities such as outdoor lighting, parking system, elevators (vertical transportation), playgrounds, site water and sewage, management offices, security offices, and so forth. Unlike single houses, MURCs with hundreds or thousands of houses tend to be planned and built all at once, under one site plan. Therefore, urban design or site planning can crucially affect the aggregated electricity consumption at neighborhood level with different land use patterns and spatial configurations, which change light, shade, wind, heat island, reflection for individual houses and public domain. Site plan attributes can also affect residents’ choice of indoor and outdoor activities. A few simulation studies tried to examine the effect of urban design at neighborhood level, but empirical analysis can be hardly found mainly due to data limitation. This study aims to empirically investigate the effect of urban design on aggregated electricity consumption at a neighborhood level. We collected the aggregated electricity consumption data of 1122 MURCs in Seoul during the first half of 2016 and adopted the panel analysis. This study confirmed that more building coverage roads and pavements increase the aggregated electricity consumption; more green open space reduces it. Interestingly, mid-rise and mid-open plan would be the optimum design because of the effect of the number of floors. As the number of floors increases, electricity consumption for vertical transportation increases faster and offsets the benefit of high-rise and high-open plan. The study is significant, because, unlike previous studies, it empirically analyzed the collective consumption of electricity at a neighborhood level, considering the site plan features. Furthermore, this study provides evidence-based implications for sustainable urban design, especially for MURCs.

The Effects of Extreme High Temperature Day-Off on Electricity Conservation

AbstractThe continuously increasing temperatures worldwide indicate the frequently extreme heat in summer will become a new normal. The extreme high temperature (EHT) could be dangerous to human health, especially for outdoor workers or commuters, and increase the risk of grid collapse. Thus, the possibility of a day-off due to EHT has started to be discussed in Taiwan, based on the experience of typhoon day-off, but not yet concluded. In this study, the effects of the EHT day-off on electricity consumption in the industrial, service, and residential sectors was investigated through two determinants: First, high temperature would increase the electricity consumption in space cooling. Second, a day-off would change people’s behavior of electricity consumption from workday to non-workday modes. Combining the effects of cooling hours and non-workdays, the net influence of the EHT day-off on electricity consumption can be evaluated. Estimated results indicated that an EHT day-off can reduce aggregate electricity consumption by between 0.41% and 1.08%. The reduction of electricity consumption due to the off-day offsets the increase driven by high temperatures. Thus, an EHT day-off will mitigate the pressure of power grid and be of benefit to electricity conservation.

Hourly charging profiles for electric vehicles and their effect on the aggregated consumption profile in Denmark

To reduce CO2 emissions, electrification of the transport sector is a central element in many countries. For Denmark, this will considerably increase the Energy Economics and System Analysis aggregated electricity consumption. How this affects the need for production-, transmission-, and distribution capacity depends on when and where the electricity is consumed. Using hourly meter data for households having an electric vehicle and for consumption at public charge stations, the novelty of this paper is that we estimate charging profiles distinguishing between types of days, months and places of charging and conclude that charging profiles for workdays and not workdays, seasons and places of charging are significantly different. That is, evaluating effects on the power system applying an average daily charging profile is too simple. Furthermore, the actual charging behaviour is more gradual than many studies assume for a dump charging behaviour and thus less problematic for peak demand.Applying the estimated profiles, formulating alternative scenarios for the charging of electric vehicles, the effect on the aggregated hourly electricity consumption in Denmark is analysed. Main conclusions are that charging of electric vehicles increases over the evening, peaks late in the evening, contributes to increase consumption during the night and is largest during the winter. That is, electric vehicles mainly consume in off-peak periods, but with 25% of the vehicles being electric in 2030 in a most favourable scenario they contribute approx. 4% to the peak in a winter workday at 6–8 pm. In the worst case they contribute 15% to the peak. In 2040 where 80% of the vehicles are electric they contribute between 15% and 55% to the annual peak consumption in Denmark.

Home Activity Pattern Estimation Using Aggregated Electricity Consumption Data

Home Activity Pattern Estimation Using Aggregated Electricity Consumption Data

Residential Electricity Consumption over the Demographic Transition in the Philippines

We performed an index decomposition analysis linked with an economic-demographic model to trace how population age structure change may affect household electricity demand in the Philippines. Our results show that population ageing has a direct, significant, and persistent effect on residential electricity demand growth. In economies like the Philippines where the elderly consumes more electricity per person relative to other cohorts, population ageing is expected to raise aggregate electricity demand through sheer compositional accounting effect. But even in economies where average electricity consumption is flat or declining in age, demographic dividends are projected to raise aggregate electricity consumption by expanding electricity access and increasing usage intensity across all age groups through a positive income effect. The permanence and irreversibility of population ageing, and the persistence of economic growth from demographic change may drive continuing growth in the energy sector.

The effect of an electricity tax on aggregate electricity consumption: evidence from Basel

We estimate the effect of an electricity tax on aggregate electricity consumption with the synthetic control method. The tax was introduced in the Swiss city of Basel in 1999 and, together with other tariff changes, increased marginal electricity prices by 5.4–8.0%. We compare the actual and a hypothetical electricity consumption in the years 1999–2006. The latter is a weighted average of electricity consumption in other Swiss cities and captures the hypothetical situation without the tax. We find a statistically insignificant effect of the tax increase of − 2.7 to − 1.9%, which implies a rather small, but not unreasonable, price elasticity of between − 0.5 and − 0.2. Ambiguous effects on average prices and an unfortunate communication by officials may explain why the innovative reform failed to induce a stronger response.

Appliance-specific feedback and social comparisons: Evidence from a field experiment on energy conservation

The provision of feedback about individual electricity consumption is a widely used approach to promote pro-environmental behavior. This form of feedback typically invokes social comparisons by informing households about their aggregate electricity consumption relative to others. While previous research has shown that aggregate consumption feedback translates into significant energy savings, the potential for further reductions may remain untapped because households lack knowledge about their appliance energy consumption patterns. In this paper, we present evidence from a field experiment, where we provide residents with feedback about their electricity consumption, specific to a high-energy use appliance (i.e. air-conditioner). We provide the relevant social norm information by varying the reference group of each resident. We find that our appliance-specific feedback is a powerful tool to curb electricity consumption. Residents significantly reduce their average air-conditioning usage by 17% in our treatment groups. Notwithstanding, our effects are not driven by comparative feedback with respect to different reference groups. We interpret this as encouraging evidence to promote the use of appliance-specific feedback to realize energy savings.

IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings.

Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.

Appliance Specific Feedback and Social Comparisons: Evidence From a Field Experiment on Electricity Saving

The provision of feedback about individual electricity consumption is a widely used approach to promote pro-environmental behavior. This form of feedback typically invokes social comparisons by informing households about their aggregate electricity consumption relative to others. While previous research has shown that aggregate consumption feedback translates into significant energy savings, the potential for further reductions may remain untapped because households lack knowledge about their appliance energy consumption patterns. In this paper, we present evidence from a field experiment, where we provide residents with feedback about their electricity consumption, specific to a high-energy use appliance (i.e. air-conditioner). We provide the relevant social norm information by varying the reference group of each resident. We find that our appliance-specific feedback is a powerful tool to curb electricity consumption. Residents significantly reduce their average air-conditioning usage by 17\% in our treatment groups. Notwithstanding, our effects are not driven by comparative feedback with respect to different reference groups. We interpret this as encouraging evidence to promote the use of appliance-specific feedback to realize energy savings.

Distributed Neuro-Dynamic Algorithm for Price-Based Game in Energy Consumption System

In this paper, a plug-in hybrid electric vehicles energy consumption system is studied. In order to protect each player’s privacy, the information exchange is going on the neighboring players, and a connected undirected graph is used to pattern the information flow between the players. Hence, it is impossible for each player to access the aggregate electricity consumption directly, which determines the electricity price. Under the noncooperative game frame, a distributed neuro-dynamic algorithm is proposed to optimize the benefit of each individual player base on the pricing strategies. A dynamic average consensus is applied to estimate the aggregate consumption and a projection neural network is employed to seek the Nash equilibrium point. The convergence of the proposed distributed algorithm is analyzed through the Lyapunov stability analysis. Finally, the effectiveness of the distributed neuro-dynamic algorithm is manifested in the simulation.

Subtask Gated Networks for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household’s aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart’s seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network’s regression output with the subtask’s classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.