The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

본 논문에서는 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 노이즈에 강한 특성을 보이는 퍼지 집합 이론을 이용한 새로운 패턴 분류기를 제안 한다. 기존 인공 신경망에 비해 학습속도가 매우 빠르며, 모델의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 학습 알고리즘을 퍼지 패턴 분류기에 적용하여 퍼지 패턴 분류기의 학습 속도와 패턴 분류 일반화 성능을 개선 한다. 제안된 퍼지 패턴 분류기의 학습 속도와 일반화 성능을 평가하기 위하여, 다양한 머신 러닝 데이터 집합을 사용한다.

Extreme Learning Machine 기반 퍼지 패턴 분류기 설계

The recent evolution of the electricity business regulation has given new possibilities to the electricity providers for formulating dedicated tariff offers. A key aspect for building specific tariff structures is the identification of the consumption patterns of the customers, in order to form specific customer classes containing customers exhibiting similar patterns. This paper illustrates and compares the results obtained by using various unsupervised clustering algorithms (modified follow-the-leader, hierarchical clustering, K-means, fuzzy K-means) and the self-organizing maps to group together customers with similar electrical behavior. Furthermore, this paper discusses and compares various techniques-Sammon map, principal component analysis (PCA), and curvilinear component analysis (CCA)-able to reduce the size of the clustering input data set, in order to allow for storing a relatively small amount of data in the database of the distribution service provider for customer classification purposes. The effectiveness of the classifications obtained with the algorithms tested is compared in terms of a set of clustering validity indicators. Results obtained on a set of nonresidential customers are presented.

Comparisons among clustering techniques for electricity customer classification

Overview and performance assessment of the clustering methods for electrical load pattern grouping

Accurate knowledge of the customers' consumption patterns represents a worthwhile asset for electricity providers in competitive electricity markets. Various approaches can be used for grouping customers that exhibit similar electrical behavior into customer classes. In this paper, we focus on two approaches for customer classification-a modified follow-the-leader algorithm and the self-organizing maps. We include an overview of basic theory for these methods and discuss the performance of the customer classification on the real case of a set of customers supplied by a distribution company. We compare the results obtained from the two approaches by means of two suitably defined adequacy indicators and discuss the potential applications of the surveyed approaches.

Load pattern-based classification of electricity customers

In competitive electricity markets, the distribution service providers have been given new degrees of freedom in formulating dedicated tariff offers to be applied to properly defined customer classes. For this purpose, they may take advantages from identifying the consumption patterns of their customers and grouping together customers exhibiting similar load diagrams. In this paper, we report on results obtained by using various unsupervised clustering algorithms (modified follow-the-leader, k-means, fuzzy k-means and two types of hierarchical clustering) and the self organising map to group together customers having a similar electrical behaviour. The customer classification is investigated in the paper by considering a set of 234 non-residential customers. Each customer is characterised by a set of values extracted from the load diagrams in a given loading condition. The effectiveness of the classifications obtained with the various algorithms has been compared in terms of a set of adequacy indicators based on properly defined metrics, some of which have been originally developed by the authors. The results show that the modified follow-the-leader and one type of hierarchical clustering exhibit better characteristics than the other algorithms in terms of adequacy.

Application of clustering algorithms and self organising maps to classify electricity customers

Load forecasting algorithms try to capture regular behaviours in historic load time series in order to perform an accurate forecast. The presence of anomalous days (holidays, working days between holidays, social events) is a serious drawback and requires a dedicated forecast. The successful application of artificial neural networks (ANN) in this field suggested the use of the Kohonen Self-Organising Map for clustering the similar load patterns and classifying day typologies. In order to evaluate the benefits of this choice, this work compares the Kohonen map with a classic clustering algorithm, both applied to grouping the daily load patterns in homogeneous sets. The information gathered by the clustered data is then applied to the 24-hour ahead load forecasting of anomalous days, by means of an ANN-based approach. The results show that the combined use of both clustering techniques allows better understanding of the anomalous load patterns.

Load pattern clustering for short-term load forecasting of anomalous days

Data size reduction techniques may be helpful in the process of categorising the electrical load consumption patterns on the basis of their shape. Starting from a macro-class of consumers defined according to certain general criteria on the type of consumers and the period of the year or week, the representative load pattern (RLP) of each consumer can be built by considering the data points in the time domain or a reduced number of features. This study exploits the effects of using the symbolic aggregate approximation (SAX) method to form the reduced set of features. The portion of the time–amplitude plane defining the RLP is partitioned into sub-portions on the basis of the characteristics of the whole data set. A specific partitioning of the time axis is proposed on the basis of the cumulative distribution function of the RLP variations in time. Each RLP is then coded according to the SAX principles. The resulting codes are then used into a hierarchical clustering procedure. The validity of the clustering results obtained by using the SAX data representation with the proposed non-uniform partitioning of the time axis is presented and discussed, including comparisons with the results obtained from other data size reduction techniques.

Federico Piglione

Papers

Comparisons among clustering techniques for electricity customer classification

Load pattern-based classification of electricity customers

Application of clustering algorithms and self organising maps to classify electricity customers

Load pattern clustering for short-term load forecasting of anomalous days

Data size reduction with symbolic aggregate approximation for electrical load pattern grouping