Electric Energy Consumption Data Research Articles

Anomaly Detection in Smart Electric Meters for Detecting Faults and Misuse of Electric Energy Consumption

The emergence of Smart Electric Meters (SEMs) has revolutionized energy management, providing real-time data collection and monitoring capabilities. However, ensuring the accuracy and security of this data presents challenges. Anomaly detection in electric energy consumption patterns is crucial for identifying issues like technical faults, erroneous billing, and misuse of electricity. Our project offers an Anomaly Detection System that leverages data analytics and machine learning to scrutinize SEM data. By analyzing historical patterns, the system distinguishes anomalies from routine fluctuations, triggering alerts when irregularities are detected. This system enhances the reliability and security of electric energy consumption data, fostering a more efficient and sustainable energy sector. Key Words: Anomaly Detection, Smart electric Meters, Fault Detection, Energy Misuse.

Identifikasi Aliran Energi Listrik pada Mesin CNC Machining Center

<p><em>CNC machine tools have been widely used in the process and manufacturing industries, which have the potential to increase the intensity of electrical energy consumption so that energy efficiency is needed. Energy efficiency is a necessity for the sustainability of the process and manufacturing industrial sector which will be related to energy costs and environmental factors. In order to carry out energy efficiency, it is necessary to identify the consumption of electrical energy by tracing and mapping the electrical energy flow to the components in a CNC machining center machine, so opportunities for energy savings can be known. This research was conducted by collecting data by monitoring energy consumption in stand-by, setting and cutting machine conditions. Cutting tests are carried out to determine the energy consumption of each machine component that affects energy use. After that it can be seen the energy flow pattern by classifying the energy user components into primary and secondary components. Based on the analysis of electrical energy consumption data during the cutting process, it is known that the significat energy users are the spindle motor, the axis feed motor (X, Y, Z) and the coolant motor.</em></p>

Impacts of COVID-19 on educational buildings energy consumption: case study of the university of Jordan

The global lockdowns adopted by many countries to combat the outbreak of the COVID-19 pandemic led to a significant transformation in the teaching methods adopted in higher education institutions toward dependence on online learning systems. Although this pandemic has placed a technical and financial burden on academic institutions to facilitate the successful transition to online learning, it provides opportunities to understand the impacts of adopting new policies and strategies to improve the efficient utilization of resources and thus reduce operational costs. The detailed analyses of the changes in energy consumption can support assessing the potential savings in electricity bills with the wide-scale adoption of online learning methods in the future as a business as usual to improve and modernize the education systems. This paper provides a detailed analysis of the electrical energy consumption of the buildings within the campus of the University of Jordan. The diversity of building types on the campus (e.g., university hospital, humanities schools, scientific schools) supports the provision of more general and robust recommendations to extend the results to other institutions, particularly in developing countries. The Energy Use Intensity (EUI) per unit area and EUI per student are employed for the first time for benchmarking the energy usage in educational buildings in Jordan. Overall, the analyses show that the total electricity consumption in 2020 was significantly lower than in 2019, with a decrease of 20.8% from 27.7 GWh in 2019 to 21.9 GWh in 2020. It is also found that the most significant reduction occurred in the humanities buildings (i.e., a 39% drop in energy consumption). However, this volume of energy reduction is still relatively low, considering the absence of students. Furthermore, the hospital has an extremely high EUI value (161 kWh/m2/year) compared to the other categories (e.g., the EUI for humanities schools is 32.5 kWh//m2/year). To conclude, the electrical energy consumption data suggests that there may be significant opportunities for energy conservation in all building categories, especially in the hospital.

Prophet-EEMD-LSTM based method for predicting energy consumption in the paint workshop

Energy conservation and preventive maintenance of equipment require the ability to accurately predict future trends in shop floor power consumption to keep track of equipment operation and abnormalities. Due to the non-linearity and uncertainty of the workshop electrical energy consumption data, it is very difficult to establish an accurate energy consumption prediction model. For the above problems, an energy consumption prediction model based on Prophet-EEMD-LSTM was proposed. To identify the period features in the energy consumption time series data, the Prophet algorithm was introduced to identify and extract the period and trend features of the energy consumption data. Then for the feature of the uncertainty of the remaining data, this paper used the ensemble empirical mode decomposition (EEMD) method to decompose the data to form the components with specific modalities. Finally, all the extracted feature data were put into the input layer of the Long short-term memory (LSTM), and the final energy consumption values were predicted using the excellent prediction performance of the LSTM on time-series data. The experimental results showed that the model proposed in this paper has high prediction performance for energy consumption data.

Análisis estadístico y modelo de pronóstico SARIMA aplicado al consumo de energía eléctrica en instalaciones universitarias

Analyzing the energy consumption behavior in buildings is essential for implementing energy-saving and efficient energy use measures without losing attention to the comfort inside the buildings. In this study, a statistical analysis and time series forecast of the energy situation of a group of buildings in a university academic unit in Mexico City was conducted. Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used for the forecast with electrical energy consumption data from 55 months. Training and test partitions were created with these data to generate two SARIMA models. The results showed a strong dependence on the school cycle of electricity consumption, in addition to a shift in the cycle in the first year of the study. The mean absolute percentage error (MAPE) for the training partitions created shows that the best fit is provided by the SARIMA (3,1,1) (1,0,0)12 model for the 48-month separation. In comparison, the SARIMA (2,1,2) (1,0,0)12 model does so for the 43-month test partition. The confidence intervals for the 7- and 12-month forecast are less wide for the SARIMA (3,1,1) (1,0,0)12 model than for the SARIMA (2,1,2) (1,0,0)12 model. Statistical analysis and time series modeling allows a better understanding of the building stock's energy performance and strengthens the energy audit to design or implement energy saving or efficient energy use measures.

Analysis of Major Temporary Electrical Equipment Consumption and Usage Patterns in Educational Buildings: Case Study

The energy use patterns of electrical appliances are more difficult to predict than energy use for heating, ventilation, and air-conditioning (HVAC) and lighting, as: (1) there are large varieties of electrical equipment (e.g., appliances, vending machines, etc.) in buildings and each serves a different function; thus, their energy consumption patterns are difficult to predict; (2) electrical appliances are scattered across buildings, most are not permanently fixed to a location, and they consume much energy. Appliances are also not centrally controlled, such as HVAC and lighting. Thus, energy consumption patterns are more difficult to predict. In addition, electrical appliances consume significant amounts of energy to influence energy consumption volatility. This case study focuses on understanding the energy consumption patterns of electrical appliances in educational buildings. This research analyzes the electrical appliances and energy consumption data from institutional buildings and the factors that drive energy consumption. The analyses show that: (1) energy consumption patterns are dependent on building characteristics and use; (2) the number of appliances in a building influences the peak electricity consumption; (3) vending machines and fridges consume significant amounts of electricity; it has been proven (by minimum building energy loads) that buildings that have more vending machines have significantly higher minimum loads than no or fewer vending machines; and (4) the energy-saving potential from desktops and monitors rose to 60 kWh during lunchtime and 500 kWh at night.

Profitability comparison of active and passive energy efficiency improvements in public buildings

Buildings and construction are responsible for about 40% of the energy and process-related CO2 emissions worldwide. At present, the renewal rate of building stock is relatively low, for example in Finland, which is the case under study in this work, it is 1.5% per year. With respect to the target of a carbon neutral world by 2050, the effect on emissions of existing building stock must be considered. In this paper, we present the results of analysis of energy efficiency improvement investment in 12 city-owned buildings in two Finnish cities. Using measured heat and electrical energy consumption data, a model was developed for analyzing the impact of active and passive energy efficiency improvement investment options per building. The examined active technologies were heat pumps, solar photovoltaic panels, and heat recovery in ventilation system, and the passive improvement compared was structural improvements in the building envelope. The results show that investments in energy efficiency improvements can reduce the yearly energy-related operating costs of the buildings by 35% on average. The study further finds that usually investment in active energy efficiency improvements provides a bigger return on investment than passive improvements. The profitability of examined measures makes Public–Private Partnership a feasible way to provide funding for renovation of public buildings.Graphical abstract

Realizing multifractality of smart meter data for household characteristic prediction

Driven by the efficient and personalized services and better management of the distribution networks, power utilities are always willing to recognize the household characteristics which reflect the dwelling and social-demographic characteristics of customers. As a key component of the distribution grid monitoring systems, smart meters installed at each customer’s household offer a non-intrusive approach to predict the household characteristics. The resolution of energy consumption data has a great impact on prediction accuracy as fine granularity data can better characterize the household energy usage profile features. However, collecting high-resolution energy consumption data may not be feasible for power utilities due to the substantial cost of upgrading the existing Advanced Metering Infrastructure (AMI). This paper aims to propose a cost-effective framework for the accurate prediction of household characteristics by realizing the multifractal characteristics of residential electric energy consumption data recorded from smart meters. First, Multiscale Multivariate Multifractal Detrended Fluctuation Analysis (MMV-MFDFA) is performed on the energy consumption data to analyze the multiscale relationships of the residential energy usage profile and reveal the multifractal structures embedded in the energy data. Then, Self-Adaptive Multifractal Interpolation (SAMI) is developed to reconstruct quasi high-resolution energy consumption data from which distinctive features relevant to household characteristics are extracted by Mathematical Morphological Decomposition (MMD). Such features are further integrated with an advanced deep learning algorithm - Deep Forest (DF) for household characteristic prediction. The proposed method is validated using real-life smart meter data from Ireland.

A Smart Home Energy Management System Utilizing Neurocomputing-Based Time-Series Load Modeling and Forecasting Facilitated by Energy Decomposition for Smart Home Automation

The key advantage of using power-utility-owned smart meters is the ability to transmit electrical energy consumption data to power utilities’ remote data centers for various purposes, such as billing. Several useful consumer-centric use cases can also be identified for the collection and further analysis of consumers’ electrical energy consumption data from smart meters. One of the use cases is home automation. Recent related solutions for home automation involving home security and healthcare depend on the installation of sensors and/or other devices such as video cameras, which have high costs for installation and annual maintenance. Because the electrical energy consumption patterns mined from smart meter data are indicative of residents’ daily life, it is possible to develop a new home automation approach based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy decomposition</i> for smart home automation. Accordingly, in this work, a smart home energy management system (SHEMS) utilizing a parallel-processing-implemented, GPU-accelerated neurocomputing-based time-series load modeling and forecasting mechanism is proposed for smart home automation. Energy decomposition is used to facilitate the time-series load modeling and forecasting mechanism, which tracks appliance-level electrical energy consumption to be quantitatively modeled from circuit-level consumption, with no intrusive deployment of networked plug-level power meters for individual electrical home appliances. For the neurocomputing approach applied in this mechanism, an autoregressive multilayer perceptron methodology is compared against a stacked long short-term memory methodology. The presented neurocomputing-based time-series load modeling and forecasting mechanism facilitated by energy decomposition is capable of predicting residents’ daily behavioral patterns by nonintrusively analyzing and modeling relevant electrical home appliances based on their past trends for smart home automation.

LABORATORY SCALE PLANT FACTORY ARTIFICIAL LIGHT: PRIMARY EVALUATION OF THE LIGHT ENVIRONMENT, RESOURCES CONSUMPTION AND YIELDS

Plant factory artificial light (PEAL) is an attractive technology for producing high-value plants and medical-grade herbs. The PFAL research and development in Thailand are in their early stages. This article aims to design and construct the lab-scale PFAL, which includes several key features such as the lighting quality, the controls of the environment, and the embedded technology monitoring system. The lab-scale PEAL must evaluate the preliminary results of the artificial light parameters, resources consumption, the plant production yield, and the correlation between LED artificial light and the plant yields. The results demonstrated that our lab-scale PFAL consists of 27 m3 cultivation volumes. White-LED T8 (4091K) artificial light emitted, on average, the PPFD of 187±23 µmol m-2 s-1 with an R:B ratio of 1.8. The lettuce production yields show a strong positive correlation with the PPFD. The lettuce production yield is 76.23 g plant-1 under the temperature of 24.05℃, RH of 79.3%, and 923 ppm of CO2 concentration with a light period of 16/8 h. The PC-based monitoring and data acquisition control worked well during the crop experiments. The environmental parameters indicated that the results did not differ from the other PFALs. Electrical energy consumption data were also reasonable at 45%, 37%, and 17% of LED artificial light, air condition units, and other types of equipment, respectively. The artificial light sources have to improve the light uniformity and increase intensity. The lab-scale PFAL is very helpful in developing students’ skills in education and disseminating the knowledge and technical design of our PFAL to academic services. For future studies, efforts will be put in developing this PFAL to achieve higher productivity by balancing the resources consumption with the cloud-based control and monitor system.

Power Consumption Predicting and Anomaly Detection Based on Transformer and K-Means

With the advancement of technology and science, the power system is getting more intelligent and flexible, and the way people use electric energy in their daily lives is changing. Monitoring the condition of electrical energy loads, particularly in the early detection of aberrant loads and behaviors, is critical for power grid maintenance and power theft detection. In this paper, we combine the widely used deep learning model Transformer with the clustering approach K-means to estimate power consumption over time and detect anomalies. The Transformer model is used to forecast the following hour’s power usage, and the K-means clustering method is utilized to optimize the prediction results, finally, the anomalies is detected by comparing the predicted value and the test value. On real hourly electric energy consumption data, we test the proposed model, and the results show that our method outperforms the most commonly used LSTM time series model.

A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability

The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emissions are extremely important to comprehend. The role of electric vehicles is imperative amid rising transport emissions. Electric vehicles pave the way towards a low-carbon economy and sustainable environment. Successful deployment of electric vehicles relies heavily on energy consumption models that can predict energy consumption efficiently and reliably. Improving electric vehicles’ energy consumption efficiency will significantly help to alleviate driver anxiety and provide an essential framework for operation, planning, and management of the charging infrastructure. To tackle the challenge of electric vehicles’ energy consumption prediction, this study aims to employ advanced machine learning models, extreme gradient boosting, and light gradient boosting machine to compare with traditional machine learning models, multiple linear regression, and artificial neural network. Electric vehicles energy consumption data in the analysis were collected in Aichi Prefecture, Japan. To evaluate the performance of the prediction models, three evaluation metrics were used; coefficient of determination ( R2), root mean square error, and mean absolute error. The prediction outcome exhibits that the extreme gradient boosting and light gradient boosting machine provided better and robust results compared to multiple linear regression and artificial neural network. The models based on extreme gradient boosting and light gradient boosting machine yielded higher values of R2, lower mean absolute error, and root mean square error values have proven to be more accurate. However, the results demonstrated that the light gradient boosting machine is outperformed the extreme gradient boosting model. A detailed feature important analysis was carried out to demonstrate the impact and relative influence of different input variables on electric vehicles energy consumption prediction. The results imply that an advanced machine learning model can enhance the prediction performance of electric vehicles energy consumption.

Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid

The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit.

Post-metering value-added services for low voltage electricity users: Lessons learned from the Italian experience of CHAIN 2

Electrical energy consumption data accessibility for low voltage end users is one of the pillars of smart grids. In some countries, despite the presence of smart meters, a fragmentary data availability and/or the lack of standardization hinders the creation of post-metering value-added services and confines such innovative solutions to the prototypal and experimental level. We take inspiration from the technology adopted in Italy, where the national regulatory authority actively supported the definition of a solution agreed upon by all the involved stakeholders. In this context, smart meters are enabled to convey data to low voltage end users through a power line communication channel (CHAIN 2) in near real-time. The aim of this paper is twofold. On the one hand, it describes the proof of concept that the channel underwent and its subsequent validation (with performances nearing 99% success rate). On the other hand, it defines a classification framework (I2MA) for post-metering value-added services, in order to categorize each use case based on both level of service and expected benefits, and understand its maturity level. As an example, we apply the methodology to the 16 use cases defined in Italy. The lessons learned from the regulatory, technological, and functional approach of the Italian experience bring us to the provision of recommendations for researchers and industry experts. In particular, we argue that a well-functioning post-metering value-added services’ market can flourish when: (i) distribution system operators certify the measurements coming from smart meters; (ii) national regulatory authorities support the technological innovation needed for setting up this market; and (iii) service providers create customer-oriented solutions based on smart meters’ data.

A Curve Fitting Modelling Approach to Forecast Long-Term Electrical Energy Consumption: Case Study of Turkey

For Turkey to achieve the targets of Vision 2023 of being in the top ten economies of the world, the eleventh National Development Plan (NDP11) focuses on ensuring uninterrupted, high-quality, sustainable, reliable and affordable energy supply. In this regard medium- and long-term energy supply-demand planning is regarded as a key input to the planning process. Medium and long-term planning is possible only when reliable forecasts are available. Using Turkey’s electrical energy consumption data from 1970 to 2015, this study presents novel Gaussian, Fourier and Exponential curve fitting and extrapolation approaches to forecast Turkey’s electrical energy consumption up to the year 2025. Major interest is put on how the model forecasts electrical energy consumption for year 2023 because this year marks a century of the establishment of the Republic of Turkey and all strategic plans are focused on how to achieve the targets as outline in Vision 2023. We evaluate the performance of the models on how best they forecast electrical energy consumption for the year 2023. Our forecasts for the year 2023 are 352.7TWh, 377.4 TWh, and 460.1TWh for the Gaussian, Fourier and Exponential models respectively which compare well with NDP11’s estimated 375.8 TWh electrical energy consumption in 2023.

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

Daylight Saving Time policy and energy consumption

Daylight Saving Time is argued to be effective in saving energy. Turkey is one of the few countries that annulled the clock changes and remained in the summertime zone in 2016. Therefore, the country provides a unique natural experiment to test and confirm this policy change. This paper studies the impact of clock changes on electric energy consumption and load shift. We use historical electrical energy consumption, electricity prices, and relevant atmospheric essential climate variables data in Turkey between 2012–2020. We adopt Multiple Linear Regression, Difference in Differences and Interrupted Time Series methodologies to analyse the historical data. This paper shows that the Daylight Saving Time policy does not lead to a measurable amount of electrical energy savings. Furthermore, it does not cause a noticeable continuous daily load shift throughout the year. We also claim that our findings should apply to countries such as the United States, India, Japan, Australia or China, and continents of Africa and South America, whose latitudes are between 42.0° north and south of the equator.

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings.

The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.

Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.

MULTIPLE LINEAR REGRESSION APPROACH FOR SHORT-TERM FORECASTING OF ELECTRIC ENERGY CONSUMPTION IN TOGO

A Linear Multiple Regression approach is used to model the energy consumption of electricity in Togo. This model is developed from the load data recorded at the electric power source stations in Togo during the period from 2016 to 2017. This model predicts four input parameters (Day of the week, the type of day (working day). or not), Hours in the day and Load data of the same time of the previous day) is used to predict the electrical energy consumption data for the period of 2018 with a MAPE of 4.4964% and a correlation coefficient R2 equal to 95.5889%.