scholarly journals Smart Meters Time Series Clustering for Demand Response Applications in the Context of High Penetration of Renewable Energy Resources

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3458
Author(s):  
Santiago Bañales ◽  
Raquel Dormido ◽  
Natividad Duro
Keyword(s):  
Time Series ◽  
Demand Response ◽  
Time Complexity ◽  
Energy Use ◽  
Series Data ◽  
Wind Generation ◽  
Smart Meters ◽  
Two Stage ◽  
Time Series Clustering ◽  
Load Shape

The variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one. In parallel, the massive introduction of smart meters allows for the use of high frequency energy use time series data to segment electricity customers according to their demand response potential. This paper proposes a smart meter time series clustering methodology based on a two-stage k-medoids clustering of normalized load-shape time series organized around the day divided into 48 time points. Time complexity is drastically reduced by first applying the k-medoids on each customer separately, and second on the total set of customer representatives. Further time complexity reduction is achieved using time series representation with low computational needs. Customer segmentation is undertaken with only four easy-to-interpret features: average energy use, energy–temperature correlation, entropy of the load-shape representative vector, and distance to wind generation patterns. This last feature is computed using the dynamic time warping distance between load and expected wind generation shape representative medoids. The two-stage clustering proves to be computationally effective, scalable and performant according to both internal validity metrics, based on average silhouette, and external validation, based on the ground truth embedded in customer surveys.

scholarly journals Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest

2019 ◽  
Author(s):  
Xiaosheng Li ◽  
Jessica Lin ◽  
Liang Zhao
Keyword(s):  
Time Series ◽  
Data Storage ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Linear Time ◽  
Series Data ◽  
Data Generation ◽  
Time Series Clustering ◽  
Group Structures

With increasing powering of data storage and advances in data generation and collection technologies, large volumes of time series data become available and the content is changing rapidly. This requires the data mining methods to have low time complexity to handle the huge and fast-changing data. This paper presents a novel time series clustering algorithm that has linear time complexity. The proposed algorithm partitions the data by checking some randomly selected symbolic patterns in the time series. Theoretical analysis is provided to show that group structures in the data can be revealed from this process. We evaluate the proposed algorithm extensively on all 85 datasets from the well-known UCR time series archive, and compare with the state-of-the-art approaches with statistical analysis. The results show that the proposed method is faster, and achieves better accuracy compared with other rival methods.

scholarly journals A Review of Subsequence Time Series Clustering

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Seyedjamal Zolhavarieh ◽  
Saeed Aghabozorgi ◽  
Ying Wah Teh
Keyword(s):  
Time Series ◽  
Pattern Recognition ◽  
Time Series Data ◽  
State Of The Art ◽  
Dna Recognition ◽  
Series Data ◽  
Time Series Clustering ◽  
Future Studies ◽  
Literature Reviews ◽  
Open Issue

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

Measuring electric energy efficiency in Portuguese households

2015 ◽  
Vol 26 (3) ◽  
pp. 407-422 ◽  
Author(s):  
Thomas Weyman-Jones ◽  
Júlia Mendonça Boucinha ◽  
Catarina Feteira Inácio
Keyword(s):  
Time Series ◽  
Energy Efficiency ◽  
Energy Demand ◽  
Energy Use ◽  
Time Series Data ◽  
Electric Energy ◽  
Electrical Energy ◽  
Series Data ◽  
Data Set ◽  
Content Type

Purpose – There is a great interest from the European Union in measuring the efficiency of energy use in households, and this is an area where EDP has done research in both data collection and methodology. This paper reports on a survey of electric energy use in Portuguese households, and reviews and extends the analysis of how efficiently households use electrical energy. The purpose of this paper is to evaluate household electrical energy efficiency in different regions using econometric analysis of the survey data. In addition, the same methodology was applied to a time-series data set, to evaluate recent developments in energy efficiency. Design/methodology/approach – The paper describes the application to Portuguese households of a new approach to evaluate energy efficiency, developed by Filippini and Hunt (2011, 2012) in which an econometric energy demand model was estimated to control for exogenous variables determining energy demand. The variation in energy efficiency over time and space could then be estimated by applying econometric efficiency analysis to determine the variation in energy efficiency. Findings – The results obtained allowed the identification of priority regions and consumer bands to reduce inefficiency in electricity consumption. The time-series data set shows that the expected electricity savings from the efficiency measures recently introduced by official authorities were fully realized. Research limitations/implications – This approach gives some guidance on how to introduce electricity saving measures in a more cost effective way. Originality/value – This paper outlines a new procedure for developing useful tools for modelling energy efficiency.

Clustering Methodology for Time Series Mining

2009 ◽  
Vol 40 (1) ◽  
pp. 81-86
Author(s):  
Pēteris Grabusts ◽  
Arkady Borisov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Similarity Measures ◽  
Longest Common Subsequence ◽  
Series Data ◽  
Time Series Clustering ◽  
Series Analysis ◽  
Time Series Mining

Clustering Methodology for Time Series MiningA time series is a sequence of real data, representing the measurements of a real variable at time intervals. Time series analysis is a sufficiently well-known task; however, in recent years research has been carried out with the purpose to try to use clustering for the intentions of time series analysis. The main motivation for representing a time series in the form of clusters is to better represent the main characteristics of the data. The central goal of the present research paper was to investigate clustering methodology for time series data mining, to explore the facilities of time series similarity measures and to use them in the analysis of time series clustering results. More complicated similarity measures include Longest Common Subsequence method (LCSS). In this paper, two tasks have been completed. The first task was to define time series similarity measures. It has been established that LCSS method gives better results in the detection of time series similarity than the Euclidean distance. The second task was to explore the facilities of the classical k-means clustering algorithm in time series clustering. As a result of the experiment a conclusion has been drawn that the results of time series clustering with the help of k-means algorithm correspond to the results obtained with LCSS method, thus the clustering results of the specific time series are adequate.

scholarly journals Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 4683-4690 ◽  
Author(s):  
Shuheng Li ◽  
Dezhi Hong ◽  
Hongning Wang
Keyword(s):  
Time Series ◽  
Demand Response ◽  
Real World ◽  
Time Series Data ◽  
Metric Learning ◽  
Fault Detection And Diagnosis ◽  
Series Data ◽  
Smart Building ◽  
Deep Metric Learning ◽  
Detection And Diagnosis

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution.

A new methodology for customer behavior analysis using time series clustering

Kybernetes ◽  
2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hossein Abbasimehr ◽  
Mostafa Shabani
Keyword(s):  
Time Series ◽  
Dynamic Behavior ◽  
Time Series Data ◽  
Customer Segmentation ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Series Data ◽  
Time Series Clustering ◽  
Content Type ◽  
Over Time

Purpose The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time. Design/methodology/approach A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers’ transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank. Findings After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment. Practical implications The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior. Originality/value In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors’ knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.

Sign in / Sign up

Export Citation Format

Share Document