TSI: Time series to imaging based model for detecting anomalous energy consumption in smart buildings

Fahim, Muhammad; Fraz, Khadija; Sillitti, Alberto

doi:10.1016/j.ins.2020.02.069

Cited by 26 publications

(8 citation statements)

References 26 publications

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“…Associations of appliances and appliance-time are important behavioral features of consumer energy usage and can define peak load / economic hours of energy use. These correlations further describe the behavioral patterns of the respective residents and their predicted comfort [25]. With the large amount of data continuous from smart meters collected, it is also of a great interest not only for utilities and energy suppliers, but also for customers to draw up such regular trends and decision-making clusters such as energy cost containment, managing demand responses and energy efficiency strategies.…”

Section: Frequent Pattern Miningmentioning

confidence: 99%

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

2021

KSII TIIS

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The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

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Section: Frequent Pattern Miningmentioning

confidence: 99%

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

2021

KSII TIIS

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“…This has led to increased interest in installing wind turbines and floating wind turbines offshore. Since offshore wind speeds tend to be faster and steadier than those on land, offshore wind farms can be made larger and generate more energy than those onshore with less physical impact [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

et al. 2022

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This paper proposes a type-2 fuzzy controller for floating tension-leg platforms in wind turbines. Its main objective is to stabilize and control offshore floating wind turbines exposed to oscillating motions. The proposed approach assumes that the dynamics of all units are completely unknown. The latter are approximated using the proposed Sugeno-based type-2 fuzzy approach. A nonlinear Kalman-based algorithm is developed for parameter optimization, and linear matrix inequalities are derived to analyze the system’s stability. For the fuzzy system, both rules and membership functions are optimized. Additionally, in the designed approach, the estimation error of the type-2 fuzzy approach is also considered in the stability analysis. The effectiveness and performance of the proposed approach is assessed using a simulation study of a tension leg platform subject to various disturbance modes.

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“…Similar to other domains like intrusion detection [10,34], security [31], and performance monitoring [42], synthetic anomalies are used to develop anomaly detection methods for energy time series [e.g., 8,20,21,27,41]. However, the inserted synthetic anomalies and their related parameters such as amplitude and quantity are generally not derived from real-world data and do not cover both energy and power, the typically recorded physical quantities.…”

Section: Introductionmentioning

confidence: 99%

Modeling and generating synthetic anomalies for energy and power time series

Turowski

Weber

Neumann

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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With the development of the smart grid, the number of recorded energy and power times series increases noticeably. This increase allows for the automation of smart grid applications such as load forecasting and load management. This automation, however, requires data that only represents the typical behavior of the system. To ensure that such data is available, detecting the anomalies often present in recorded data is important. As a result, anomaly detection methods are a recent research topic. However, their development is often limited by undefined anomaly characteristics and a lack of labeled anomalous data. To overcome this challenge, we propose a method that generates synthetic anomalies based on real-world anomalies that can be inserted into energy and power time series. For this, we analyze real energy and power time series to identify four types of commonly occurring anomalies. Given the identified anomaly types, we formally model each type and use these models to insert synthetic anomalies of each type into arbitrary energy or power time series. We show that our method is not only capable of generating synthetic anomalies with real-world properties, but also beneficial for training supervised anomaly detection methods. CCS CONCEPTS• Computing methodologies → Anomaly detection.

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TSI: Time series to imaging based model for detecting anomalous energy consumption in smart buildings

Cited by 26 publications

References 26 publications

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

Modeling and generating synthetic anomalies for energy and power time series

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