6,786 research outputs found
A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles
[EN] Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.Serrano-Guerrero, X.; Escrivá-Escrivá, G.; Luna-Romero, S.; Clairand, J. (2020). A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles. Energies. 13(5):1-23. https://doi.org/10.3390/en13051046S123135Hong, T., Yang, L., Hill, D., & Feng, W. (2014). Data and analytics to inform energy retrofit of high performance buildings. Applied Energy, 126, 90-106. doi:10.1016/j.apenergy.2014.03.052Ogunjuyigbe, A. S. O., Ayodele, T. R., & Akinola, O. A. (2017). User satisfaction-induced demand side load management in residential buildings with user budget constraint. 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The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity distributed. The predominant research direction is
employing artificial intelligence to predict whether a customer causes NTL.
This paper first provides an overview of how NTLs are defined and their impact
on economies, which include loss of revenue and profit of electricity providers
and decrease of the stability and reliability of electrical power grids. It
then surveys the state-of-the-art research efforts in a up-to-date and
comprehensive review of algorithms, features and data sets used. It finally
identifies the key scientific and engineering challenges in NTL detection and
suggests how they could be addressed in the future
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System-level key performance indicators for building performance evaluation
Quantifying building energy performance through the development and use of key performance indicators (KPIs) is an essential step in achieving energy saving goals in both new and existing buildings. Current methods used to evaluate improvements, however, are not well represented at the system-level (e.g., lighting, plug-loads, HVAC, service water heating). Instead, they are typically only either measured at the whole building level (e.g., energy use intensity) or at the equipment level (e.g., chiller efficiency coefficient of performance (COP)) with limited insights for benchmarking and diagnosing deviations in performance of aggregated equipment that delivers a specific service to a building (e.g., space heating, lighting). The increasing installation of sensors and meters in buildings makes the evaluation of building performance at the system level more feasible through improved data collection. Leveraging this opportunity, this study introduces a set of system-level KPIs, which cover four major end-use systems in buildings: lighting, MELs (Miscellaneous Electric Loads, aka plug loads), HVAC (heating, ventilation, and air-conditioning), and SWH (service water heating), and their eleven subsystems. The system KPIs are formulated in a new context to represent various types of performance, including energy use, peak demand, load shape, occupant thermal comfort and visual comfort, ventilation, and water use. This paper also presents a database of system KPIs using the EnergyPlus simulation results of 16 USDOE prototype commercial building models across four vintages and five climate zones. These system KPIs, although originally developed for office buildings, can be applied to other building types with some adjustment or extension. Potential applications of system KPIs for system performance benchmarking and diagnostics, code compliance, and measurement and verification are discussed
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
Non-technical losses (NTL) occur during the distribution of electricity in
power grids and include, but are not limited to, electricity theft and faulty
meters. In emerging countries, they may range up to 40% of the total
electricity distributed. In order to detect NTLs, machine learning methods are
used that learn irregular consumption patterns from customer data and
inspection results. The Big Data paradigm followed in modern machine learning
reflects the desire of deriving better conclusions from simply analyzing more
data, without the necessity of looking at theory and models. However, the
sample of inspected customers may be biased, i.e. it does not represent the
population of all customers. As a consequence, machine learning models trained
on these inspection results are biased as well and therefore lead to unreliable
predictions of whether customers cause NTL or not. In machine learning, this
issue is called covariate shift and has not been addressed in the literature on
NTL detection yet. In this work, we present a novel framework for quantifying
and visualizing covariate shift. We apply it to a commercial data set from
Brazil that consists of 3.6M customers and 820K inspection results. We show
that some features have a stronger covariate shift than others, making
predictions less reliable. In particular, previous inspections were focused on
certain neighborhoods or customer classes and that they were not sufficiently
spread among the population of customers. This framework is about to be
deployed in a commercial product for NTL detection.Comment: Proceedings of the 19th International Conference on Intelligent
System Applications to Power Systems (ISAP 2017
Ranking Volatility in Building Energy Consumption Using Ensemble Learning and Information Entropy
Given the rise in building energy consumption and demand worldwide, energy inefficiency detection has become extremely important. A significant portion of the energy used in commercial buildings is wasted as a result of poor maintenance, degradation or improperly controlled equipment. Most facilities employ sensors to track energy consumption across multiple buildings. Smart fault detection and diagnostic systems use various anomaly detection techniques to discover point anomalies in consumption. While these systems work reasonably well in detecting equipment anomalies over short-term intervals, further exploration is needed in finding methods that consider long-term consumption to detect anomalous buildings. This paper presents a novel approach for a multi-building campus to rank and visualize the long-term volatility of building consumption. This allows for the optimal allocation of limited time and resources for the detection and resolution of energy waste. The proposed method first classifies daily consumption into 5 classes using an ensemble learner and then calculates the information entropy on the resulting classification set to determine volatility. The ensemble learner receives input from a K-Nearest Neighbor classifier, a Random Forest classifier and an Artificial Neural Network. In general, buildings are expected to keep the same energy profile over time, all else being equal. Buildings that frequently change energy profiles are ranked and flagged by the system for review, which would call for the next step to reduce waste and costs and to increase the sustainability of buildings. Data on energy consumption for 132 buildings is obtained from energy management at the Georgia Institute of Technology. Experimental results show the effectiveness of the proposed approach
Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart
sensors installed in residential buildings. If leveraged properly, that data
could assist end-users, energy producers and utility companies in detecting
anomalous power consumption and understanding the causes of each anomaly.
Therefore, anomaly detection could stop a minor problem becoming overwhelming.
Moreover, it will aid in better decision-making to reduce wasted energy and
promote sustainable and energy efficient behavior. In this regard, this paper
is an in-depth review of existing anomaly detection frameworks for building
energy consumption based on artificial intelligence. Specifically, an extensive
survey is presented, in which a comprehensive taxonomy is introduced to
classify existing algorithms based on different modules and parameters adopted,
such as machine learning algorithms, feature extraction approaches, anomaly
detection levels, computing platforms and application scenarios. To the best of
the authors' knowledge, this is the first review article that discusses anomaly
detection in building energy consumption. Moving forward, important findings
along with domain-specific problems, difficulties and challenges that remain
unresolved are thoroughly discussed, including the absence of: (i) precise
definitions of anomalous power consumption, (ii) annotated datasets, (iii)
unified metrics to assess the performance of existing solutions, (iv) platforms
for reproducibility and (v) privacy-preservation. Following, insights about
current research trends are discussed to widen the applications and
effectiveness of the anomaly detection technology before deriving future
directions attracting significant attention. This article serves as a
comprehensive reference to understand the current technological progress in
anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table
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