scholarly journals A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 237
Author(s):  
Roberto Chiosa ◽  
Marco Savino Piscitelli ◽  
Alfonso Capozzoli
Keyword(s):  
Anomaly Detection ◽  
Data Analytics ◽  
Low Voltage ◽  
Medium Voltage ◽  
Related Data ◽  
Level Data ◽  
Energy Behavior ◽  
Combining Data ◽  
Energy Information System ◽  
Detection And Diagnosis

Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance and spotting valuable saving opportunities. More and more researchers worldwide are focused on the development of more robust frameworks of analysis capable of extracting from meter-level data useful information to enhance the process of energy management in buildings, for instance, by detecting inefficiencies or anomalous energy behavior during operation. This paper proposes an innovative anomaly detection and diagnosis (ADD) methodology to automatically detect at whole-building meter level anomalous energy consumption and then perform a diagnosis on the sub-loads responsible for anomalous patterns. The process consists of multiple steps combining data analytics techniques. A set of evolutionary classification trees is developed to discover frequent and infrequent aggregated energy patterns, properly transformed through an adaptive symbolic aggregate approximation (aSAX) process. Then a post-mining analysis based on association rule mining (ARM) is performed to discover the main sub-loads which mostly affect the anomaly detected at the whole-building level. The methodology is developed and tested on monitored data of a medium voltage/low voltage (MV/LV) transformation cabin of a university campus.

Reduction of the Low Voltage Substation Constraints by Inserting Photovoltaic Systems in Underserved Areas

2019 ◽  
Vol 12 (2) ◽  
pp. 105-112
Author(s):  
Benbouza Naima ◽  
Benfarhi Louiza ◽  
Azoui Boubekeur
Keyword(s):  
Low Voltage ◽  
Voltage Drop ◽  
Electrical Energy ◽  
Utilization Rate ◽  
Photovoltaic Systems ◽  
Voltage Distribution ◽  
Medium Voltage ◽  
Pv Systems ◽  
Transformer Substation ◽  
Load Pattern

Background: The improvement of the voltage in power lines and the respect of the low voltage distribution transformer substations constraints (Transformer utilization rate and Voltage drop) are possible by several means: reinforcement of conductor sections, installation of new MV / LV substations (Medium Voltage (MV), Low Voltage (LV)), etc. Methods: Connection of mini-photovoltaic systems (PV) to the network, or to consumers in underserved areas, is a well-adopted solution to solve the problem of voltage drop and lighten the substation transformer, and at the same time provide clean electrical energy. PV systems can therefore contribute to this solution since they produce energy at the deficit site. Results: This paper presents the improvement of transformer substation constraints, supplying an end of low voltage electrical line, by inserting photovoltaic systems at underserved subscribers. Conclusion: This study is applied to a typical load pattern, specified to the consumers region.

scholarly journals Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings

2021 ◽  
Vol 11 (2) ◽  
pp. 500
Author(s):  
Fabrizio Pilo ◽  
Giuditta Pisano ◽  
Simona Ruggeri ◽  
Matteo Troncia
Keyword(s):  
Energy Demand ◽  
Data Analytics ◽  
Distribution Systems ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
A Priori ◽  
Energy Transition ◽  
Smart Meters ◽  
Large Databases ◽  
A New Technique

The energy transition for decarbonization requires consumers’ and producers’ active participation to give the power system the necessary flexibility to manage intermittency and non-programmability of renewable energy sources. The accurate knowledge of the energy demand of every single customer is crucial for accurately assessing their potential as flexibility providers. This topic gained terrific input from the widespread deployment of smart meters and the continuous development of data analytics and artificial intelligence. The paper proposes a new technique based on advanced data analytics to analyze the data registered by smart meters to associate to each customer a typical load profile (LP). Different LPs are assigned to low voltage (LV) customers belonging to nominal homogeneous category for overcoming the inaccuracy due to non-existent coincident peaks, arising by the common use of a unique LP per category. The proposed methodology, starting from two large databases, constituted by tens of thousands of customers of different categories, clusters their consumption profiles to define new representative LPs, without a priori preferring a specific clustering technique but using that one that provides better results. The paper also proposes a method for associating the proper LP to new or not monitored customers, considering only few features easily available for the distribution systems operator (DSO).

scholarly journals Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4133
Author(s):  
Alessandro Bosisio ◽  
Matteo Moncecchi ◽  
Andrea Morotti ◽  
Marco Merlo
Keyword(s):  
Machine Learning ◽  
Distribution System ◽  
Low Voltage ◽  
Regression Tree ◽  
Distribution Networks ◽  
Machine Learning Algorithms ◽  
Extreme Weather Events ◽  
Medium Voltage ◽  
Weather Events ◽  
Building Area

Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

scholarly journals Series Active Filter Design Based on Asymmetric Hybrid Modular Multilevel Converter for Traction System

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 134 ◽  
Author(s):  
Muhammad Ali ◽  
Muhammad Khan ◽  
Jianming Xu ◽  
Muhammad Faiz ◽  
Yaqoob Ali ◽  
...  
Keyword(s):  
Low Voltage ◽  
Filter Design ◽  
Switching Frequency ◽  
Modular Multilevel Converter ◽  
Multilevel Converter ◽  
Medium Voltage ◽  
Converter Topologies ◽  
Asymmetric Hybrid ◽  
Simulation Results ◽  
Traction System

This paper presents a comparative analysis of a new topology based on an asymmetric hybrid modular multilevel converter (AHMMC) with recently proposed multilevel converter topologies. The analysis is based on various parameters for medium voltage-high power electric traction system. Among recently proposed topologies, few converters have been analysed through simulation results. In addition, the study investigates AHMMC converter which is a cascade arrangement of H-bridge with five-level cascaded converter module (FCCM) in more detail. The key features of the proposed AHMMC includes: reduced switch losses by minimizing the switching frequency as well as the components count, and improved power factor with minimum harmonic distortion. Extensive simulation results and low voltage laboratory prototype validates the working principle of the proposed converter topology. Furthermore, the paper concludes with the comparison factors evaluation of the discussed converter topologies for medium voltage traction applications.

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