Electricity Consumption Patterns Research Articles

Residential Electricity Consumption Patterns and their Relationship to Commute Times by Mode

In this study, the correlation between emerging electricity consumption patterns in the Chicago area and commute times to work by mode are explored. We found that when aggregating similar daily electricity consumption curves, areas sharing similar electricity patterns are geographically closer to each other and located in regions that are strictly urban or suburban. Furthermore, we modelled these clusters in terms of commute times to work by mode. The findings suggest that morning electricity consumption differs significantly among clusters and that it is caused primarily by long duration public transport trips.

A Line Loss Separation Method Based on Topology-aware Correlations and Nonlinear Impedance

Due to the varying order and hierarchical relationship of different meters on the same line, the variation pattern of line loss and electricity consumption is not obvious, making it difficult to separate them accurately. To address this issue, an innovative method for line loss separation based on topological relationship perception and nonlinear impedance is proposed. Firstly, based on the topology recognition algorithm, we identify the order of the electricity meters and the relationship between the father and child-level electricity meters. Then, using the principle of topology relationship, we accurately measure the impact of the father and child level electricity meters on the overall line loss electricity quantity. Combined with data difference and matrix difference methods, the fixed loss value is offset. On this basis, a sliding window method is used to draw the curve change of nonlinear impedance and loss of electricity quantity. By analyzing the approximate continuous relationship between electricity consumption and line loss rate through curves, the line loss electricity consumption can be effectively separated. The results indicate that the proposed method has a better performance compared to other methods.

Cooling-related electricity consumption patterns for small and medium businesses in California: Current impacts and future projections under climate change

Cooling-related electricity consumption patterns for small and medium businesses in California: Current impacts and future projections under climate change

Evaluating the effects of the COVID-19 pandemic on electricity consumption patterns in the residential, public, commercial and industrial sectors in Sweden

The COVID-19 pandemic has had drastic effects on societies around the world. Due to restrictions or recommendations, companies, industries and residents experienced changes in their routines and many people shifted to working from home. This led to alterations in electricity consumption between sectors and changes in daily patterns. Understanding how various properties and features of load patterns in the electricity network were affected is important for forecasting the network's ability to respond to sudden changes and shocks, and helping system operators improve network management and operation. In this study, we quantify the extent to which the COVID-19 pandemic has led to shifts in the electricity consumption patterns of different sectors in Sweden. The results show that working from home during the pandemic has led to an increase in the residential sector's total consumption and changes in its consumption patterns, whereas there were only slight decreases in the industrial sector and relatively few changes in the public and commercial sectors. We discuss the reasons for these changes, the effects that these changes will have on expected future electricity consumption patterns, as well as the effects on potential demand flexibility in a future where working from home has become the new norm.

Small Firm Electricity Demand in Las Cruces, New Mexico, USA

Research examining small commercial and industrial electricity usage patterns have historically received less attention than residential electricity consumption patterns. This study examines electricity as an input to small firm commercial and industrial (CIS) production in Las Cruces, the second largest metropolitan economy in the state of New Mexico, using annual frequency data from 1978 to 2018. Those data include labor, per capita personal income, price measures for electricity and natural gas, and weather variables. The long-run and short-run elasticities of the data are then estimated using an autoregressive distributed lag model (ARDL). In the long-run, the CIS derived-demand curve is found to be upward sloping, and Las Cruces CIS customers use natural gas as a complementary input. Real per capita income is also found to have a positive impact in the long-run, while weather impacts are found to be ambiguous. In the short-run, the Las Cruces CIS derived-demand curve is downward sloping, CIS customers use natural gas as a substitute factor, and weather extremes are found to be positively correlated with small firm electricity usage.

From blackouts to breakthroughs: Examining electricity's relevance in healthcare during COVID-19 and the future role of renewable energy

Power supply played a critical role during the COVID-19 pandemic in keeping hospitals and critical medical equipment operating, which was quickly reflected in electricity consumption and new consumption patterns. However, not all countries faced the same challenges. While developed countries could react adequately to changes in hospital electricity consumption patterns, low-income countries faced infrastructure and energy access challenges during the pandemic, which hampered the management of the pandemic in all of its dimensions. This paper reviews the importance of power supply in hospitals through a literature review and divides the discussion into three sections. First, the factors influencing new hospital consumption patterns during the pandemic are discussed. Then, the consequences of the lack of electricity in remote areas on managing the pandemic are reviewed. Finally, the impact of the pandemic on the design of hybrid power generation systems is reviewed, which must not only provide autonomy for similar future scenarios but also be prepared to respond to sudden increases in power consumption due to a greater number of patients or longer working hours, an increase in the number of critical loads, and possible restrictions on access to fuel. Thus, this work provides valuable insights for policymakers, academics, and researchers on the importance of updating hospital power supply standards and contributes to demonstrating the need to electrify remote hospitals.

Implications of the COVID-19 Pandemic on China's Power Consumption: A comprehensive Analysis Derived from Monthly Electricity Consumption Forecasts

The COVID-19 pandemic profoundly altered the total and pattern of electricity consumption across Chinese industries and households. However, current research has not fully and accurately measured the impact of the pandemic. This paper builds a novel discrete gray seasonal model (referred to as the FDDGSM model) to predict the scenario of different domestic electricity-consuming sectors and residential electricity consumption without a pandemic backdrop, by comparing it with the actual electricity consumption in 2020, the impact of the COVID-19 pandemic on China's electricity consumption is better revealed. Through empirical analysis, the following conclusions are reached in this study: (1) The novel discrete grey season model shows more stable and accurate characteristics in forecasting time series with seasonal trends. (2) The pandemic did not significantly impact electricity consumption in primary industries and households, while actual usage in secondary and tertiary industries was significantly lower than expected in a non-pandemic scenario. (3) The impact on secondary and tertiary industries varied in magnitude and duration, and they differed in the rate of recovery in electricity consumption.

Analysis of residential electricity consumption patterns utilizing smart-meter data: Dubai as a case study

Analysis of residential electricity consumption patterns utilizing smart-meter data: Dubai as a case study

Implication of machine learning techniques to forecast the electricity price and carbon emission: Evidence from a hot region

The current study examines the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price accurately. The main contribution is focused on eastern region of Saudi Arabia, a relatively hottest geographical area full of energy resources but with different electricity consumption patterns. The relative irrelevance of temperature as predicting factor of electricity consumption is quite surprising and contradicts the previous studies. In the eastern region, electricity price has negative association with electricity consumption. While comparing traditional and machine learning, it is found that machine learning techniques offer better predictability. Amongst the machine learning techniques, the support vector machine has the lowest errors in forecasting the electricity price. Additionally, the support vector machine approach is used to forecast the trend of carbon emissions caused by electricity consumption. The findings have policy implications and offer valuable suggestions to policymakers while addressing the determinants of electricity consumption and forecasting electricity prices.

A universal optimization framework for commercial building loads using DERs from utility tariff perspective with tariff change impacts analysis

To accommodate the changes in the nature and the pattern of electricity consumption with the available resources, utilities have introduced variety of tariffs over the years. This paper develops a comprehensive optimization framework that addresses the challenges associated with the diversity of utility tariffs for commercial loads. Optimization of commercial building net load through Battery Energy Storage System (BESS) and renewable energy resources is modeled and explored for minimizing billing cost for different tariffs. Cost functions are formulated for each possible commercial utility tariff type, engendering the universal cost function format. An algorithm is developed to apply the optimization model and generate the desired optimal outputs (e.g., optimal net load and BESS power, costs and savings, etc.). The results for several building loads and all tariff types are compared and analyzed to present the findings. An impacts analysis is performed to observe how different changes in tariffs affect the optimizations results. Results exhibit that for same building load and renewable generation, the tariff type can have an overwhelming impact on costs and savings. Tariffs without Time of Use (TOU) charges or peak demand charge components offer lower savings opportunity from BESS optimization. Price change sensitivities showed the complicated dynamics among TOU price components on savings and historical trend analysis as an effective tool to predict future savings. Tradeoff between energy and demand charge heavy tariff options are also discussed for user benefit. The recent change in TOU time periods presents limited desired benefits while the modified tariff proposed in this paper exhibit significant improvement in terms of utility operation.

Integrated Demand Response Programs in Energy Hubs: A Review of Applications, Classifications, Models and Future Directions

In the traditional power system, customers respond to their primary electricity consumption pattern based on price or incentive to take additional advantages. By developing energy hubs (EHs) where electricity, heat, natural gas and other forms of energy are coupled together, all types of energy customers, even the inelastic loads, can participate in the demand response (DR) program. This novel vision has led to the concept of “integrated demand response (IDR)”. IDR programs (IDRPs) in EHs involve coordinating multiple DR activities across different energy systems, such as buildings, industrial complexes and transportation networks. The main purpose of IDR is so that multi-energy users can respond not only by shifting or reducing their energy consumption from the demand side, but also by changing the type of energy consumed in response to the dispatching center. The integration of IDRPs in EHs can help to reduce energy costs, improve grid stability and increase the penetration of renewable energy sources (RES) in the power system. Moreover, by synchronizing DR activities across different energy systems, IDRPs can provide additional benefits, such as improved energy efficiency, reduced greenhouse gas emissions and increased resilience to power outages and other disruptions. In this paper, we provide an overview of the IDRP across EH areas, encompassing different aspects of it. First, the nature behind IDRP and its basic concept is introduced. Then, a categorization of fundamental principles within the IDRP is undertaken. Furthermore, modelling formulation and optimization techniques of IDRP in EHs are conducted. In addition to the IDRP content and model, this article deals with the research performed in this field from different perspectives. Finally, the advantages and prospect challenges of IDRPs are discussed.

Addressing the Prevailing Energy Crisis in Sri Lanka: A Case for Cost-Efficient Energy Consumption

The economic crisis in Sri Lanka has significantly influenced people's lives, leading to shortages of basic needs such as medicine, food, and fuel. The prevailing situation has taken the country further from achieving sustainable development goal 7, which is to provide affordable, reliable, sustainable, and modern energy for all. Despite the potential for renewable energy, Sri Lanka's key energy sources are primarily oil and coal. Therefore, the government had to revise the electricity tariffs recently in order to cover the coal expenditure. As a result, most people are still unaware of the electricity bill value they expect at the end of the month. This study covers a suggested web-based system that focuses on micro-level aspects of the problems mentioned above. The monthly electricity bill prediction is a vital part of this system which guides users to follow a cost-effective electricity consumption pattern. The system also carries out an online donation program aiming to give long-term energy solutions to recognised rural communities, thereby addressing the issue of energy poverty in these regions. The work done to incorporate these technologies into a web application is further explained in this research with architectural diagrams. Furthermore, literature reviews were conducted to convey comparisons and interpret the results to reach a decisive conclusion.

Joint load prediction of multiple buildings using multi-task learning with selected-shared-private mechanism

Short-term load forecasting for residential buildings is of great significance in ensuring the safe and economic operation of the power grid. However, most existing prediction methods focus solely on the temporal characteristics of individual buildings and ignore the spatial correlation between different buildings in a neighborhood. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with a selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto-correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy is proposed to build input feature set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, a novel selected-shared-private mechanism is designed for multi-task learning to improve the prediction accuracy, which can selectively utilize information from related tasks while learning private features. The proposed model is compared with other methods on the public dataset, and the results demonstrate that the proposed model can make satisfactory joint predictions for multiple buildings with more than 35% accuracy improvement over the support vector machine.

Optimization of the electricity consumption behaviors of users under uncertain electricity prices and consumption patterns

Real-time electricity prices and customer demands are inextricably linked in the smart grid environment. For the purpose of reducing grid load peaks, enhancing grid security, and optimizing customers’ electricity bills, it is critical to model dynamic real-time electricity prices and flexible customer requests, along with providing an optimized plan to guide users’ decisions regarding the use of household appliances and quantify their needs. The solution to this problem, however, is not straightforward due to the following factors. Firstly, the real-time electricity price is constantly changing, so it must be estimated from grid data associated with it. Furthermore, there are often some variations in the actual power consumption of appliances due to the uncertainty of human behavior, the characteristics of appliances, and the influence of the environment. In addition, when optimizing, serious problems such as tripping and circuit overload must be taken into account. Therefore, considering random electricity prices and random electricity consumption, in the paper, we present the models to simulate real-time changes in electricity prices and energy consumption behaviors, as well as a method to help users optimize their use of household appliances and quantify their requirements with lower electricity cost. By analyzing the experimental results, we demonstrate that our work can be used to provide a scheduling scheme for appliances that minimizes electricity costs and reduces grid pressure.

Firms and Households during the Pandemic: What Do We Learn from Their Electricity Consumption?

We analyze the impact of the COVID-19 pandemic on electricity consumption patterns. We highlight the importance of decomposing total electricity consumption into consumption by firms and by households to better understand the economic and social impacts of the crisis. While electricity demand by firms has fallen substantially, the demand by households has gone up. In particular, our focus is on Spain where, during the total lockdown, these effects reached −29% and +10% respectively, controlling for temperature and seasonality. While the electricity demand reductions during the second wave were milder, the demand by firms remained 5% below its normal levels. We also document a change in people's daily routines in response to the stringency of the lockdown measures, as reflected in their hourly electricity consumption patterns.

IOT BASED REDUCTION OF ELECTRICITY THEFT

The Internet of Things (IoT) has emerged as a promising technology to address various challenges in different domains. One of the critical challenges in the power sector is the issue of electricity theft, which results in huge revenue losses for power utilities. To avoid such issues, the proposed system monitors the real-time electricity consumption of a household or commercial building using a current sensor and sends the data to a cloud- based IoT platform, ThingSpeak, using NodeMCU and WiFi. The platform analyzes the data and identifies any unusual electricity consumption patterns, which could be indicative of electricity theft. Keywords- IOT (Internet of Things), Electricity, EnergyMeter Coil, ESP8266, ThingSpeak.

Learning matrix profile method for discord-based attribution of electricity consumption pattern behavior

This paper frames itself in analyzing complex electricity load measurements to attribute the consumption behavior to a specific entity. Attribution may be seen as the first step in several grid-based activities, like energy management, privacy, and identification of illicit activities. This work proposes and tests a novel approach that utilizes consumption discords as the analysis carrier for attributing multiple load patterns to specific consumers. The discord-driven analysis is performed utilizing the synergism of the Matrix Profile with each of two well-established supervised learning classification methods: the K-Nearest-Neighbor and Support-Vector-Machine. The proposed approach is applied in attributing electricity consumption pattern behaviors to a set of academic institutions that are comprised of multiple academic units. Notably, multiple units within the same entity exhibit different consumption behavior, thus imposing a high challenge in attributing the unit’s consumption behavior to its entity of origin when there is a variety of targeted entities. Obtained results demonstrate that the load discord-based MP-KNN and MP-SVM combinations provide higher identification accuracy than the state-of-the-art method of performing supervised classification with a feed-forward artificial neural network of the full load patterns. The identification accuracy of the proposed method outperformed the ANN load classification by approximately 13%.

Distribution Loss Analysis of NEA, Nepal

Nepal Electricity Authority (NEA) is facing the problems in supply chain of electricity that causes the loss of energy which results in loss of economy. NEA has planned to extend the distribution network throughout the country so that every household can have access of Electrical Energy (EE) by 2023. NEA is also working in the sector of maximizing the sales in order to increase its revenue. In order to achieve such goals NEA must enhance its distribution system infrastructure. In this article, distribution losses are to be analyzed for enhancing and upgradation of existing electrical network if any. Some of the electrical components that cause electrical loss in distribution sector are also to be analyzed. This helps on determination of causes of losses. As NEA is importing electrical energy from India, consumer’s sales energy should be analyzed. At present calibration of area specific electricity consumption pattern that too on Time of Day (TOD) basis is not possible. This research paper will determine the process that helps in identifying area specific consumption pattern thus aid operators in laying reliable supply chain infrastructure to meet the demand of electricity. Electrical loss to be minimized by enhancing distribution network as per consumer demand.

Similarity-Based Optimization Framework for Curtailment Service Providers Through Collaborative Filtering and Generalized Dynamic Factor Model

We propose a novel demand response framework based on two similarities in building electricity consumption patterns for curtailment service providers (CSP)s to optimally manage the aggregated building energy. We find the first similarity in spatio-temporal correlations of building demand. To avoid conflicts of interests among buildings, we synthesize building demand and renewable energy scenarios based on that similarity by using the modified generalized dynamic factor model. We find the second similarity in hidden cost functions of discomfort indices for temperature and light. The cost function of a new building is extracted through the collaborative filtering based on the second similarity. Extracted cost functions allow us to align electricity and discomfort costs in the objective function and to cluster buildings to reduce computation time. Two similarities are used to improve the co-optimized day-ahead and real-time market decision process and respond to regulation signals through a two-stage stochastic optimization. Based on building energy statistics, we verify the optimality, efficiency, and comfortability of our strategy by showing that it has a lower cost, longer time period when residents feel comfortable, and lower computation time than existing strategies, particularly when a new building is aggregated.

The impact of co-adopting electric vehicles, solar photovoltaics, and battery storage on electricity consumption patterns: Empirical evidence from Arizona

Electric vehicles, residential rooftop solar photovoltaics, and home battery storage contribute to a reliable, resilient, affordable, and clean power grid. To accelerate decarbonization, large-scale deployment of these distributed technologies will be indispensable but cause significant impacts on the power grid in the future. This study provides the first empirical evidence of the impact of co-adopting the three technologies on electricity consumption patterns based on smart meter data of three representative adopters from Arizona. The estimated overall impact of the coadoption on average hourly net load is -0.68 kWh. An intraday consumption transfer is identified. Leveraging the three technologies, consumers reduced electricity consumption from the grid during the day and early evening, increased consumption in the late evening, and exported excess electricity to the grid during the day. We also estimate the decomposed impacts of each adopted technology.