Clustering residential electricity load curve via community detection in network

Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.

Clustering and classification of energy meter data: A comparison analysis of data from individual homes and the aggregated data from multiple homes

The transition towards a more sustainable environment requires the development of new control systems on the demand side to integrate renewable energy sources into the energy systems.For this purpose,energy meter data of homes have been broadly used in modelling,forecast and optimal control of energy use.However,usability and reliability of household energy meter data have not been specifically addressed.In this study,we apply commonly used machine learning methods on the heating consumption data of(1)two individual homes in an apartment building and(2)the district heating substation of the apartment building which includes 72 homes,to identify how the characteristics of data affect the result of data analysis.Two clustering approaches were applied using the K-means algorithm to group similar heating daily profiles.Using the clustering results,different classification algorithms such as logistic regression and random forest were applied to predict the heating consumption level with regards to the weather conditions.The data analysis process showed that the substation data which is the aggregated heating consumption of the 72 homes is more reliable and valid for energy prediction than the data from two individual homes.This is due to the large variation and uncertainty in the daily energy use of individual homes.

Evaluating the effects of the COVID-19 pandemic on electricity consumption patterns in the residential,public,commercial and industrial sectors in Sweden

The COVID-19 pandemic has had drastic effects on societies around the world.Due to restrictions or recom-mendations,companies,industries and residents experienced changes in their routines and many people shifted to working from home.This led to alterations in electricity consumption between sectors and changes in daily patterns.Understanding how various properties and features of load patterns in the electricity network were affected is important for forecasting the network’s ability to respond to sudden changes and shocks,and helping system operators improve network management and operation.In this study,we quantify the extent to which the COVID-19 pandemic has led to shifts in the electricity consumption patterns of different sectors in Sweden.The results show that working from home during the pandemic has led to an increase in the residential sector’s total consumption and changes in its consumption patterns,whereas there were only slight decreases in the industrial sector and relatively few changes in the public and commercial sectors.We discuss the reasons for these changes,the effects that these changes will have on expected future electricity consumption patterns,as well as the effects on potential demand flexibility in a future where working from home has become the new norm.