Normal Consumption Patterns Research Articles

Simulation of Malfunctions in Home Appliances’ Power Consumption

Predicting errors in home appliances is crucial for maintaining the reliability and efficiency of smart homes. However, there is a significant lack of such data on appliance malfunctions that can be used in developing effective anomaly detection models. This research paper presents a novel approach for simulating errors of heterogeneous home appliance power consumption patterns. The proposed model takes normal consumption patterns as input and employs advanced algorithms to produce labeled anomalies, categorizing them based on the severity of malfunctions. One of the main objectives of this research involves developing models that can accurately reproduce anomaly power consumption patterns, highlighting anomalies related to major, minor, and specific malfunctions. The resulting dataset may serve as a valuable resource for training algorithms specifically tailored to detect and diagnose these errors in real-world scenarios. The outcomes of this research contribute significantly to the field of anomaly detection in smart home environments. The simulated datasets facilitate the development of predictive maintenance strategies, allowing for early detection and mitigation of appliance malfunctions. This proactive approach not only improves the reliability and lifespan of home appliances but also enhances energy efficiency, thereby reducing operational costs and environmental impact.

Improving Electricity Theft Detection Using Electricity Information Collection System and Customers’ Consumption Patterns

Electricity theft detection (ETD) techniques employed to identify fraudulent consumers often fail to accurately pinpoint electricity thieves in real time. The patterns associated with electricity use are leveraged to identify anomalies indicative of electricity theft. However, challenges in the benchmark ETD include overfitting and a high incidence of false positives (FPs) resulting from incorrect usage patterns formed by considering only electricity consumption patterns without accounting for external factors that contribute to variations in normal consumption patterns. Further investigation is required to precisely detect instances of electricity theft, thereby minimizing nontechnical losses and forecasting future electricity demand to maintain a stable supply. This study employs a master energy meter located on the transformer side to monitor the amount of energy distributed to the region. Zones with a high likelihood of energy theft are detected by comparing the sum of readings from all the smart meters with the readings from the master energy meter installed on the HV of the substation transformer within the same electric feeder. Ensemble XGBoost machine-learning algorithm and K-Means algorithm are used for the classification of malicious and nonmalicious samples and grouping similar types of consumers together, respectively. This makes the proposed model resistant to false-positive rates caused by changes in usage patterns that aren’t done on purpose. Furthermore, energy thieves are identified by detecting anomalies in consumption behavior while maintaining constant internal and external environmental variables. This novel model proposed here mitigates the FP rate found in the present research of electricity usage data. Our approach outperforms support vector machines, convolution neural network, and logistic regression in simulations. Precision, F1-score, recall, Matthews Correlation Coefficient, Receiver Operating Characteristics (ROC)-Area Under The Curve (AUC), and Precision Recal (PR)-Area Under The Curve (AUC) validate our model. The simulation results show that the proposed K-Means-LSTM-XGBoost model improved the classifier’s F1-score, precision, and recall to 93.75%, 95.16%, and 92.38%, respectively. Our model classifies huge time series data with high precision and can be utilized by the utility for real time electricity theft detection.

Motivators and facilitators of fruit and vegetable intake in Tonga

Food choice is complex and is heavily influenced by the environment one lives in(1). Pacific Island food environments, including those in Tonga, have changed considerably in recent years, making healthier food choice more challenging(2). A widespread nutrition transition across the region has contributed to an increase in the availability of, and accessibility to, highly-processed foods, and high rates of diet-related non-communicable diseases(3). While system change is needed to support the availability, accessibility and affordability of healthy foods, nutrition education plays an important role in supporting individuals, communities, and populations to navigate their rapidly changing food environments, and to encourage healthy food choice and behaviour change. Approaches to nutrition education in the Pacific Islands region vary and do not always consider the socio-cultural aspects of the food environment, especially when focusing on fruit and vegetable consumption. This work was driven by an intent to develop contextually appropriate nutrition education plans using a structured process, Design Online(4). However, to develop a nutrition education plan a critical analysis of the current motivators and facilitators for the behaviour are required. When reviewing the scientific literature there is limited information on determinants of food choice within Tonga, and more broadly within the Pacific Islands context. Therefore, the aim of this cohort study was to qualitatively explore and document the motivating and facilitating determinants of fruit and vegetable consumption in Tonga. Data collection took place during August and September 2023 on the main island of Tongatapu. Semi-structured interviews (n = 5 men, 3 women) and a focus group (n = 4 women) based on the most appropriate method of engaging with participants, were conducted in Tongan. Guiding questions were derived from Design Online and proposed within the following categories: motivating determinants and facilitating determinants. Interview responses were qualitatively analysed using an inductive content analysis model. Key categories for motivating determinants included health and nutrition knowledge, normal consumption patterns, availability and access, production, financial considerations and preferences, perceptions and practices. Key categories for facilitating determinants included education, community engagement, environmental factors, food preference, finance, and accessibility. While this work has explored motivating and facilitating determinants for fruit and vegetable consumption in Tonga with a small sample, it makes an important contribution to the limited literature. The findings of this study can be used to underpin activities, such as the design of nutrition education plans. The findings also provide a foundation for further exploration of determinants of food choice. This study was undertaken on the main island of Tongatapu, but it is of interest to explore determinants with communities who live in the outer islands, and at different time points during the year to reflect seasonality.

Renewable-based load shifting system for demand response to enhance energy-economic-environmental performance of industrial enterprises

A market characterized by economic uncertainty and increased competition has led industrial enterprises to find ways to improve operational efficiency and reduce energy costs. In this context, the Demand Response (DR) has drawn ever greater attention from industries, allowing the shift of the electricity usage from their normal consumption patterns in response to changes in the price of electricity over time. This study proposes an innovative control strategy for renewable-based Load Shifting (LS) system designed on, at the same time, energy, economic, and environmental performance to realize an effective DR for industrial enterprises. The proposed techno-economic model is based on power flow management, economic indicators, such as NPV, IRR, and DPP, and associated CO2 emissions, adopting both deterministic and stochastic approaches. A realistic government incentive program framed in a potential carbon-based market is also assessed and validated. The resulting solution is obtained through a sensitivity analysis of several parameters, including the operating conditions of 15%, 20%, 30%, 35%, and 45% LS. MATLAB-Simulink simulations provide the following sizes for both storage and photovoltaic systems of 38, 68, 87, 115, 153 kWh and 18, 18, 32, 43, 61 kWp, respectively. Results show the profitability of the proposed DR, with a payback period that ranges from 6 to 10 years and within 3 years when incentives are considered. NPV and IRR range from 34,261 to 101,428 € and from 7% to 15.9%, respectively. The amount of CO2 emissions is reduced up to 47.7% for the highest LS percentage. Finally, the investigated incentive program led to CO2 prices between 18.2 and 551.9 €/tonCO2. Finally, results from the stochastic analysis show a good consistence with deterministic results.

Energy Consumption Auditing Anomaly Detection System of Robotic Manipulators based on a Generative Adversarial Network

Unexpected anomalies pose significant risks to the health and security of intelligent manufacturing systems. This paper proposes a generative adversarial network (GAN)-based anomaly detection framework specifically for monitoring robotic manipulator operation using a side-channel energy auditing mechanism. To tackle the limitation arising from the lack of labeled data, the GAN model is trained by a semi-supervised learning approach that identifies anomalies during online operations as outliers. The overfitting is purposely utilized during the model training to enlarge the difference between normal energy consumption patterns used for training and anomalous profiles in real-time testing. In addition, the GAN model is modified to use multiple discriminators to analyze the individual energy profile associated with each joint or motor. The anomaly is detected by evaluating the mean and standard deviation values of anomaly scores' distribution, and both values are continuously updated by Welford's algorithm in real time to take into account the effect of environmental variations during operations. The detection performance on our custom dataset demonstrates the feasibility of the proposed pipeline. Specifically, for physical attacks, the framework can achieve an accuracy of approximately 0.93 for instant-wise detection and 0.84 for event-wise detection.

Arctic Indigenous Women Consume Greater than Acceptable Levels of Organochlorines

Exposure to polychlorinated biphenyls and organochlorine pesticides through traditional food resources was examined for Arctic Indigenous women living in two cultural and environmental areas of the Canadian Arctic—one community representing Baffin Island Inuit in eastern Arctic and two communities representing Sahtú Dene/Métis in western Arctic. Polychlorinated biphenyls, toxaphene, chlorobenzenes, hexachlorocyclohexanes, dichlorodiphenyltrichloroethane, chlordane-related compounds and dieldrin were determined in local food resources as normally prepared and eaten. Quantified dietary recalls taken seasonally reflected normal consumption patterns of these food resources by women in three age groups: 20–40 y, 41–60 y and ≥ 61 y. There was wide variation of intake of all organochlorine contaminants in both areas and among age groups for the Sahtú. Fifty percent of the intake recalls collected from the Baffin Inuit exceeded the acceptable daily intake for chlordane-related compounds and toxaphene, and a substantial percentage of the intake records for dieldrin and polychlorinated biphenyls exceeded the acceptable or tolerable daily intake levels. Primary contributing foods to organochlorine contaminants intake for the Baffin Inuit were meat and blubber of ringed seal, blubber of walrus and mattak and blubber of narwhal. Important foods contributing organochlorine contaminant to the Sahtú Dene/Métis were carlbou, whitefish, inconnu, trout and duck. The superior nutritional benefits and potential health risks of traditional food items are reviewed, as are implications for monitoring organochlorine contaminant contents of food, clinical symptoms and food use.

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

This work studies the automatic classification of water consumption patterns and electrical devices, both supervised and unsupervised. This involves training machine learning algorithms to identify normal and abnormal water consumption patterns and differentiate between different types of electrical devices. We performed an unsupervised classification of consumer water patterns in direct and indirect ways. The first is to use the raw consumption patterns obtained directly from the server. The second one corresponds to the use of the sampled consumption patterns. This classification is performed using hierarchical bottom-up classification and a self-organizing map. A probabilistic analysis of daily water consumption is performed to extract the percentage of daily consumption with the most information. It enables us to identify water consumption patterns more quickly by reducing the number of data points for each daily pattern, allowing us to recognize and classify anomalous behaviors as soon as feasible. Then, the signatures of electrical devices are classified using three ML algorithms: multilayer perceptron (MLP), k nearest neighbor (KNN), and decision tree (DT). Furthermore, assembly approaches are also studied. These are based on the OAA (One Against All) principle, which presents one class against all other classes, and the ECOC (Error-Correcting Output Codes) philosophy, which allows the classification error to be corrected. According to the bias/variance trade-off, both techniques enhanced classification by ensuring accuracy and generalization. A more in-depth analysis of the properties of the electrical devices is handled with an esemplastic approach based on gradient-boosting decision trees. The features of electrical devices can be extracted using this analysis based on the nature of their harmonic signatures

A Covert Electricity-Theft Cyberattack Against Machine Learning-Based Detection Models

The advanced metering infrastructure in modern networked smart homes brings various advantages, such as multiple pricing and energy scheduling. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of employed ML algorithms. Given a group of smart homes, in this article, we present a covert electricity theft strategy through mimicking normal consumption patterns and compromising neighboring meters concurrently. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we design a feature extraction scheme that can capture the correlation between attacks and honest customers. Finally, we develop a new deep learning-based detection model. Extensive experiments based on real-world datasets show that the presented attack could evade existing mainstream detectors but still gain high profits. In addition, the proposed countermeasure outperforms state-of-the-art detection methods.

A Comprehensive Review of Demand-Side Management Based on Analysis of Productivity: Techniques and Applications

Demand-side management (DSM) includes various persuasive measures to improve the use of energy; thus, it has been studied from various perspectives in the literature. Nowadays, the context of productivity has an important role in the evaluation of the electrical energy systems. Accordingly, this paper presents a platform to comprehensively contemplate the DSM from the productivity perspective that features its three aspects. First, the widespread indices of DSM are manifestly redefined, and a plenary index of DSM is introduced, reflecting both energy and investment productivity. Second, the modification of energy efficacy and consumption pattern is discussed, considering a general categorization of DSM modalities based on the pertaining index of each branch. Third, a modified model of demand response (DR) is developed to implement seven DR strategies in the smart microgrids. The simulation results demonstrate that the load factor can improve up to 8.12% with respect to the normal consumption pattern. Moreover, the load factor can be further enhanced at least by 4.22% in comparison with the customary model.

POLA KONSUMSI ZAT GIZI MAKRO DAN AKTIVITAS FISIK ANAK USIA 13-15 TAHUN

In their daily life, player at the age of teenagers have unhealthy lifestyles such as lack of intake of macronutrients which consist of carbohydrates, fast and protein which are not balanced with physical activity, if not balanced it will lead to problems in nutrition, every player has nutritional consumption and different activities differences that create dissimilarities between players, as well as a lack of knowledge to start the process of maturity or change. The purpose of this study was to determine the consumption pattern of macronutrients and physical activity in player who took part in futsal extracurricular activities at SMP Negeri 5 Sidoarjo. Using a descriptive method of 2 variables with a quantitative approach, members of the study with a sample of 30 students. The technique of collecting data is by using a porposive sampling method or a method of collecting data with certain considerations. How to analyze the sample using the Physical Activity Questionnare-Adolescents (PAQ-A) and 24-hour food recall assisted by the nutrisurvey application, done by pouring frequency into percentage from. From the survey results of 30 samples at most, there were 46,70% deficit energy intake with 14 player, 50,00% excess protein consumption with 15 players, 56,70% normal fat consumption with 17 players, the same 50,00% carbohydrate consumption less and normal. The result of the physical activity analysis showed 70% in the low category with 21 player. With normal consumption pattern of macronutrients, it is necessary to increase them and physical activity with moderate results. Physical activity of a player obtains additional adequate and balanced nutrition, it appears to have a good nutritional consumption pattern.

Application of distinct demand response program during the ramping and sustained response period

The environmental concerns around energy, namely electricity, have driven attention to innovative approaches to fostering consumers participation in the whole energy system management. Accordingly, the concept of demand response provides incentives and signals no consumers to change the normal consumption patterns to increase the use of renewables, for example. The problem is that such response of consumers has a large amount of uncertainty. This paper proposes a methodology in which different demand response programs are activated and deactivated during an event to cover the demand response deviations from the target. Even after achieving the response target, if the actual response of consumers is reduced to a critical level, additional programs are activated. The proposed approach considers consumers participating in an aggregate way, supported by an aggregator. The case study in this paper accommodates three demand response programs, showing how different consumers are activated and remunerated for the provision of consumption reduction. It has been seen that the proposed methodology is flexible as desired to accommodate the uncertainty of consumers’ responses.

Nontechnical Loss Detection With Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Multilayer Classifier in a Smart Grid

The combined Duffing–Holmes (D–H)–based quantizer and one-dimensional (1D) convolutional neural network (CNN)-based multilayer classifier were applied to perform the nontechnical loss (NTL’s) (electricity fraud) feature quantification, feature extraction, and classification tasks to analyze electricity consumption data and to identify either normal or abnormal consumption patterns for NTL detection. The metering data is gathered every 15 minutes and a 3-hour screening window is used to distinguish between the normal conditions and likely NTL events or power outage events. The D–H-based quantizer in the feature quantification layer may quantify the different levels among three events for preliminary screening differences using D–H self-synchronization dynamic errors. In the feature extraction layer, two 1D convolutional-pooling processes are used to extract 1D key feature signals to enhance the distinguished levels for further classification applications. The gray relational analysis (GRA)-based multilayer network is trained as a classifier. In the classification layer, to identify electricity fraud events. The proposed method is verified and validated using simulation data and the electricity fraud attack model. The correlation coefficient and unitary averaged changed intensity index are applied in correlation analysis to discover apparent abnormality between historical consumption and metering consumption patterns within the short-time monitoring. The D-H-based quantizer and 1D CNN-based classifier then work together to accomplish classification tasks on the time-series metering data. The experimental results show that the suggested classifier model demonstrates promising performance and efficiency compared to the traditional multilayer classifier in feature extraction, training, and recall processing, and accurate classification.

The Canadian pork industry and COVID‐19: A year of resilience

While COVID‐19 had the potential to be extremely disruptive to the Canadian pork supply chain, the sector showed resiliency by adjusting to market changes to ensure industry continuation. Unlike other non‐agricultural firms that were mandated to close at times, the pork sector was deemed an essential service and allowed to continue operating throughout the pandemic. Evidence of this resiliency is seen in three main ways. First, market access to the United States was maintained for both live pigs and pork exports. Second, Canada not only maintained market share in global pork exports, but it also actually increased shipments because of strong demand from China caused by African swine fever. Third, the challenges of processing plant closures and labour shortages were overcome in a variety of ways including increasing interprovincial shipments and increasing live pig exports to the United States. Pork consumption on a per capita basis continued the historical downward trend, and it is expected that consumers will return to their normal consumption patterns (e.g., dining at restaurants) despite job losses. At the meat processing level, it is anticipated that there will be an acceleration in the process to automate.

Engaging customers in demand response programs: The role of reward and punishment in customer adoption in Switzerland

It is challenging to engage customers in demand response programs, which require significant interventions in customers’ normal energy consumption patterns. Moreover, little is known about how customers can be motivated to adopt innovations that promote collective benefits. This research investigates the effectiveness of the two most basic elements of incentive-based policies — reward and punishment — with regard to customer participation in the sustainable energy domain. We counter the prevailing assumptions in innovation and technology research that favor the use of reward rather than punishment to engage customers. Based on a series of experimental studies, we find mixed evidence. It appears that punishment is at least as effective as reward in engaging customers in sustainable technology innovations. Even more importantly, both reward and punishment are shown to overcome concerns relating to technology. Moderated mediation reveals that economic incentives and disincentives ameliorate critical obstacles because customers more strongly favor their own personal benefit over the collective benefit and are more willing for ‘self-serving’ reasons to adopt technology that contributes to collective benefits. The findings of this research thus have novel practical and theoretical implications for energy innovation involving customers.

The Financial Response to the COVID-19 Pandemic

We are living through extraordinary times as the United States has struggled to deal with the global COVID-19 pandemic, and as of the writing of this paper, we remain in the midst of the crisis. We still do not know what the full economic and financial consequences of the pandemic will be, but they are likely to persist for an extended period, as many people are unlikely to return to normal work or consumption patterns soon, and household and business defaults are likely to increase and negatively affect the financial sector. This paper, written to assist faculty in teaching about the pandemic, focuses on key actions taken by the financial regulators in response to the crisis so far, giving a detailed summary of the actions taken by the Federal Reserve, the Treasury Department, and Congress. We discuss the Federal Reserve’s monetary policy actions, emergency lending facilities, and supervisory forbearance by the federal banking agencies. We also provide a summary of financial provisions of the CARES Act, including an analysis of the Paycheck Protection Program. We explore a number of central themes already emerging, including the blurry line between monetary policy and fiscal policy. We also highlight the fact that unlike the Financial Crisis of 2008, today’s economic crisis is caused by the failure to take sufficient public health actions to contain a global pandemic, not poor policy and risk choices in the financial markets; the fact that the crisis is caused by a public health failure poses unique problems for economic and financial policymakers in crafting responses.

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, in this paper, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

Flexibility Scheduling for Large Customers

Large customers are considered as major flexible electricity demands which can reduce their electricity costs by choosing appropriate strategies to participate in demand response programs. However, practical methods to aid the large customers for handling the complex decision making process for participating in the programs have remained scarce. This paper proposes a novel decision-making tool for enabling large customers to determine how they adjust their electricity usage from normal consumption patterns in expectation of gaining profit in response to changes in prices and incentive payments offered by the system operators. The proposed model, formulated as a mixed-integer linear programming problem, simultaneously determines the optimal integration of the flexibility options including flexible load rescheduling and utilizing onsite generation and energy storage systems, along with energy procurement from the grid that allows the large customers to optimize their energy portfolio from different sources including bilateral contracts and the market. The characteristics of the proposed integrated flexibility scheduling and energy procurement model and its benefits are investigated through several case studies conducted on a test large industrial load.

Risk management and participation planning of electric vehicles in smart grids for demand response

Demand response (DR) can serve as an effective tool to better balance the electricity demand and supply in the smart grid. It is defined as "the changes in electricity usage by end-use customers from their normal consumption patterns" in response to pricing and incentive payments. This paper focuses on new opportunities for DR with electric vehicles (EVs). EVs are potential distributed energy resources that support both the grid-to-vehicle and vehicle-to-grid modes. Their participation in the time-based (e.g., time-of-use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Smart scheduling of EV charging and discharging activities also supports high penetration of renewables with volatile energy generation. This paper proposes a novel stochastic model from the Independent System Operator's perspective for risk management and participation planning of EVs in the smart grid for DR. The risk factors considered in this paper involve those caused by uncertainties in renewables (wind and solar), load patterns, parking patterns, and transmission lines' reliability. The effectiveness of the model in response to various settings such as the area type (residential, commercial, and industrial), the EV penetration level, and the risk level has been investigated.

Electricity Theft Detection in AMI Using Customers’ Consumption Patterns

As one of the key components of the smart grid, advanced metering infrastructure brings many potential advantages such as load management and demand response. However, computerizing the metering system also introduces numerous new vectors for energy theft. In this paper, we present a novel consumption pattern-based energy theft detector, which leverages the predictability property of customers’ normal and malicious consumption patterns. Using distribution transformer meters, areas with a high probability of energy theft are short listed, and by monitoring abnormalities in consumption patterns, suspicious customers are identified. Application of appropriate classification and clustering techniques, as well as concurrent use of transformer meters and anomaly detectors, make the algorithm robust against nonmalicious changes in usage pattern, and provide a high and adjustable performance with a low-sampling rate. Therefore, the proposed method does not invade customers’ privacy. Extensive experiments on a real dataset of 5000 customers show a high performance for the proposed method.

Guest Editorial New Trends of Demand Response in Smart Grids

The papers in this special section focus on technological and system developments in designing power grids. The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. Now, traditional grids are facing kinds of challenges, and the world is proposing to modernize legacy and make strides toward smart grid. It is widely recognized that demand response is the core feature of smart grid, which can be formally defined as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. With the support of the advanced information and communication technologies, demand response is able to improve the efficiency, reliability, economics, and sustainability of power generation, distribution, and utilization.