Approaches for Monitoring the Energy Consumption with Machine Learning Methods

2015 ◽  
Vol 805 ◽  
pp. 79-85
Author(s):  
Christian Gebbe ◽  
Johannes Glasschröder ◽  
Gunther Reinhart
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Anomaly Detection ◽  
Energy Demand ◽  
Sensor Data ◽  
Detection Methods ◽  
Energy Costs ◽  
Threshold Values ◽  
Production Methods ◽  
Static Threshold

In times of rising energy costs and increasing customer awareness of sustainable production methods, many manufacturers take measures to reduce their energy consumption. However, after the realization of such activities the energy demand often tends to increase again due to e.g. leaks, clogged filters, defect valves or suboptimal parameter settings. In order to prevent this, it is necessary to quickly identify such increases by continuously monitoring the energy consumption and counteracting accordingly. Currently, the monitoring is either performed manually or by setting static threshold values. The manual control can be time consuming for large amounts of sensor data. By setting static threshold values only a fraction of the inefficiencies are disclosed. Another option is to use anomaly detection methods from the area of machine learning, which compare the actual sensor values with the expected ones. In this paper an overview about existing anomaly detection methods, which can be applied for this purpose, is presented.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

scholarly journals Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 302
Author(s):  
Chunde Liu ◽  
Xianli Su ◽  
Chuanwen Li
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
Underground Mining ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Clustering Methods ◽  
Safety Warning

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

scholarly journals FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

2021 ◽  
Vol 116 ◽  
pp. 30-48
Author(s):  
Bram Steenwinckel ◽  
Dieter De Paepe ◽  
Sander Vanden Hautte ◽  
Pieter Heyvaert ◽  
Mohamed Bentefrit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Data Streams ◽  
Expert Knowledge ◽  
Root Cause Analysis ◽  
Sensor Data ◽  
Cause Analysis ◽  
Root Cause

scholarly journals Implementation of the ISO 50001 standard to sustainable energy and economic saving the industrial sector

2020 ◽  
Vol 25 (2) ◽  
pp. 261-268
Author(s):  
Guillermo Valencia ◽  
Katherin Nahomy Rodriguez ◽  
Gloria Raquel Torregroza Matos ◽  
Carlos Acevedo ◽  
Jorge Duarte Forero
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Gap Analysis ◽  
Energy Performance ◽  
Industrial Sector ◽  
Energy Costs ◽  
Working Pressure ◽  
Economic Saving ◽  
Save Energy

Given the growth in energy demand, the limited energy resources, and the high environmental impact of energy generation from fossil fuels, it is vital to find methods to obtain save energy costs in different sectors, such as residential, industrial, transportation sector, and domestic. This paper presents a methodology that allows the implementation of an energy management system following the guidelines of the ISO 50001 standard. A gap analysis was performed to determine the position of the organization with respect to the requirements of the standard, and the next step was the inspection of the plant to find opportunities for improvement that would lead to energy optimization. From the results, six equipment was the cause of the 82% of the energy consumption in the production process, and some recommendation was proposed with the aim to optimize energy consumption. A methodology is proposed for the standard implementation, which can be implemented by different organizations from different fields to achieve savings in energy costs in the plant. Some relevant actions to improve the energy performance of the plant were proposed, such as the optimization of the compressed air system, the reduction of potential numbers of leakage, and the reduction of the working pressure of the system.

scholarly journals Recent Advances in Anomaly Detection Methods Applied to Aviation

Aerospace ◽  
2019 ◽  
Vol 6 (11) ◽  
pp. 117 ◽  
Author(s):  
Luis Basora ◽  
Xavier Olive ◽  
Thomas Dubot
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Advances ◽  
Flight Operations

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud

2021 ◽  
Vol 11 (1) ◽  
pp. 52-72
Author(s):  
Rajendra Kumar Dwivedi ◽  
Rakesh Kumar ◽  
Rajkumar Buyya
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Anomaly Detection ◽  
Gaussian Distribution ◽  
Supervised Machine Learning ◽  
Sensor Data ◽  
Support Vector ◽  
Learning Scheme ◽  
Healthcare Monitoring ◽  
Sensor Cloud

Smart information systems are based on sensors that generate a huge amount of data. This data can be stored in cloud for further processing and efficient utilization. Anomalous data might be present within the sensor data due to various reasons (e.g., malicious activities by intruders, low quality sensors, and node deployment in harsh environments). Anomaly detection is crucial in some applications such as healthcare monitoring systems, forest fire information systems, and other internet of things (IoT) systems. This paper proposes a Gaussian distribution-based supervised machine learning scheme of anomaly detection (GDA) for healthcare monitoring sensor cloud, which is an integration of various body sensors of different patients and cloud. This work is implemented in Python. Use of Gaussian statistical model in the proposed scheme improves precision, throughput, and efficiency. GDA provides 98% efficiency with 3% and 4% improvements as compared to the other supervised learning-based anomaly detection schemes (e.g., support vector machine [SVM] and self-organizing map [SOM], respectively).

scholarly journals Energy Audit System for Households using Machine Learning

2021 ◽  
Vol 10 (7) ◽  
pp. 33-36
Author(s):  
Nagesh* A.
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Random Forest ◽  
Energy Demand ◽  
Predictive Accuracy ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Energy Audit ◽  
Household Level ◽  
Audit System

the growth in population and economics the global demand for energy is increased considerably. The large amount of energy demand comes from houses. Because of this the energy efficiency in houses in considered most important aspect towards the global sustainability. The machine learning algorithms contributed heavily in predicting the amount of energy consumed in household level. In this paper, a energy audit system using machine learning are developed to estimate the amount of energy consumed at household level in order to identify probable areas to plug wastage of energy in household. Each energy audit system is trained using one machine leaning algorithm with previous power consumption history of training data. By converting this data into knowledge, gratification of analysis of energy consumption is attained. The performance of energy audit Linear Regression system is 82%, Decision Tree system is 86% and Random Forest 91% are predicted energy consumption and the performance of learning methods were evaluated based on the heir predictive accuracy, ease of learning and user friendly characteristics. The Random Forest energy audit system is superior when compare to other energy audit system.

Energieorientierte Produktionssteuerung*/Energy-oriented production control

2016 ◽  
Vol 106 (03) ◽  
pp. 152-156
Author(s):  
C. Schultz ◽  
S. Braunreuther ◽  
G. Prof. Reinhart
Keyword(s):  
Energy Consumption ◽  
Production Planning ◽  
Energy Supply ◽  
Energy Demand ◽  
Production Control ◽  
Load Management ◽  
Energy Costs ◽  
Production Planning And Control ◽  
Planning And Control ◽  
And Control

Angesichts steigender Energiekosten sowie eines zunehmenden Bewusstseins für nachhaltige Produktion ist es heute erforderlich, Zielvorgaben für den Energieverbrauch in der Produktionsplanung und -steuerung zu verankern sowie umzusetzen. Aus diesem Grund präsentiert dieser Artikel ein Verfahren für eine energieorientierte Produktionssteuerung, die auf der Basis von Energieflexibilität und Lastmanagement den Energiebedarf der Produktion mit einem begrenzten Energieangebot synchronisiert.   Due to rising energy costs and a growing awareness for sustainable production, it is now necessary for companies to establish targets for energy consumption in production planning and control. Therefore, this article illustrates a method for energy-oriented production control on the basis of flexibility and load management which synchronizes the energy demand in manufacturing with a limited energy supply.

scholarly journals Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments

2020 ◽  
Author(s):  
Victoria Jayne Mawson ◽  
Ben Richard Hughes
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Demand ◽  
Large Data ◽  
Electrical Energy ◽  
Building Energy ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Unseen Data ◽  
Manufacturing Environments

Abstract Manufacturing remains one of the most energy intensive sectors, additionally, the energy used within buildings for heating, ventilation and air conditioning (HVAC) is responsible for almost half of the UK’s energy demand. Commonly, these are analysed in isolation from one another. Use of machine learning is gaining popularity due to its ability to solve non-linear problems with large data sets and little knowledge about relationships between parameters. Such models use relationships between inputs and outputs to make further predictions on unseen data, without requiring any understanding regarding the system, making them highly suited to dealing with the stochastic data sets found in a manufacturing environment. This has been seen in literature for determining electrical energy demand for residential or commercial buildings, rather than manufacturing environments. This study proposes a novel method of coupling simulation with machine learning to predict indoor workshop conditions and building energy demand, in response to production schedules, outdoor conditions, building behaviour and use. Such predictions can subsequently allow for more efficient management of HVAC systems. Based upon predicted energy consumption, potential spikes were identified and manufacturing schedules subsequently optimised to reduce peak energy demand. Coupling simulation techniques with machine learning algorithms eliminates the requirement for costly and intrusive methods of data collection, providing a method of predicting and optimising building energy consumption in the manufacturing sector.

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