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Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Tri Kurniawan Wijaya, Matteo Vasirani, Samuel Humeau, Karl Aberer
2015 2015 IEEE International Conference on Big Data (Big Data)  
Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households  ...  While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential  ...  ACKNOWLEDGMENT The authors would like to thank Alhussein Fawzi and the anonymous reviewers for their helpful comments and discussion.  ... 
doi:10.1109/bigdata.2015.7363836 dblp:conf/bigdataconf/WijayaVHA15 fatcat:kwkl7dcrmbgr3mr3brwtb6xaqm

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

Yi Wang, Qixin Chen, Tao Hong, Chongqing Kang
2018 IEEE Transactions on Smart Grid  
The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected.  ...  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.  ...  [42] Non-residential building smart meter data; Area, weather, and primary use type data; 507 Every 1 hour 2014/12-2015/11 UMass Smart [43] Residential electricity consumption data; 400 Every  ... 
doi:10.1109/tsg.2018.2818167 fatcat:yacc5fol6vhydkw2azgy4mpfai

Segmenting Residential Smart Meter Data for Short-Term Load Forecasting

Alexander Kell, A. Stephen McGough, Matthew Forshaw
2018 Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy '18  
This paper aims to improve the accuracy of load-forecasting by using machine learning techniques to predict 30 minutes ahead using smart meter data.  ...  These findings suggest that clustering smart meter data prior to forecasting is an important step in improving accuracy when using machine learning techniques.  ...  ., we focus on client-side prediction using smart meter data. We were, therefore, able to cluster the data based on load profile, as opposed to grouping based on geographical location.  ... 
doi:10.1145/3208903.3208923 dblp:conf/eenergy/KellMF18 fatcat:y4rpwvp52nbspnmxw4shnd4hqi

A review on clustering of residential electricity customers and its applications

Amin Rajabi, Li Li, Jiangfeng Zhang, Jianguo Zhu, Sahand Ghavidel, Mojtaba Jabbari Ghadi
2017 2017 20th International Conference on Electrical Machines and Systems (ICEMS)  
In this regard, this paper aims at reviewing the new research for clustering techniques for residential customers.  ...  In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting.  ...  The recent changes in electricity system structure toward smart grids and the great utilization of advanced metering infrastructure (AMI) and smart meters have affected this trend greatly.  ... 
doi:10.1109/icems.2017.8056062 fatcat:xgpy7kqvjjazbkx6p7dk5m75em

Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid

Heung-gu Son, Yunsun Kim, Sahm Kim
2020 Energies  
for demand forecasting based on clustering.  ...  This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering.  ...  To overcome the unequal time-series of each residential smart meter, they suggested model-based clustering to compute parallel data for large samples.  ... 
doi:10.3390/en13092377 fatcat:cje5ybi2lrh6viwoss2m3xn3yu

Clustering-Based Residential Baseline Estimation: A Probabilistic Perspective

Mingyang Sun, Yi Wang, Fei Teng, Yujian Ye, Goran Strbac, Chongqing Kang
2019 IEEE Transactions on Smart Grid  
Case studies have been conducted on fine-grained smart meter data collected from a real dynamic timeof-use (dTOU) tariffs trial of the Low Carbon London (LCL) project.  ...  Demand Response (DR) is one of the most costeffective solutions for providing flexibility to power systems.  ...  Case studies are conducted based on the real smart meter data from the LCL dTOU trial.  ... 
doi:10.1109/tsg.2019.2895333 fatcat:75mwelup2jdmveyyqfqvs5pubm

Artificial intelligence techniques for enabling Big Data services in distribution networks: A review

Sara Barja-Martinez, Mònica Aragüés-Peñalba, Íngrid Munné-Collado, Pau Lloret-Gallego, Eduard Bullich-Massagué, Roberto Villafafila-Robles
2021 Renewable & Sustainable Energy Reviews  
for renewable power plants (like wind and solar large scale power plants), grid stability and reliability at transmission and distribution level, demand forecasting, demandside management, optimized energy  ...  Considering the potential of the data collected in electrical networks, the scientific community is applying and developing AI techniques for power system applications [9] .  ...  MAE MAPE RMSE Other Aggregated demand forecasting DNN, RF Short-term US 2019 Electricity consumption for residential buildings for the next day.  ... 
doi:10.1016/j.rser.2021.111459 fatcat:43mrjxzeijhrpll35ifzyihtde

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations [article]

Stephen Haben, Siddharth Arora, Georgios Giasemidis, Marcus Voss, Danica Vukadinovic Greetham
2021 arXiv   pre-print
Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level.  ...  Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties.  ...  In [168] , an MLR model is used for forecasting aggregate smart meter data from a utility in Canada.  ... 
arXiv:2106.00006v2 fatcat:rb2yrt4tsjap3jhrb7dz76dg2e

Electricity demand forecasting by multi-task learning

Jean-Baptiste Fiot, Francesco Dinuzzo
2017 2017 IEEE Manchester PowerTech  
Our study is based on residential and industrial smart meter data provided by the Irish Commission for Energy Regulation (CER).  ...  We explore the application of kernel-based multitask learning techniques to forecast the demand of electricity measured on multiple lines of a distribution network.  ...  For this section, we built a low-rank OKL model using the data from all smart meters.  ... 
doi:10.1109/ptc.2017.7980955 fatcat:wkkghrg4vzeiff5onxauhb4kfm

Multi-nodal short-term energy forecasting using smart meter data

Barry P. Hayes, Jorn K. Gruber, Milan Prodanovic
2018 IET Generation, Transmission & Distribution  
This paper deals with the short-term forecasting of electrical energy demands at the local level, incorporating advanced metering infrastructure (AMI), or 'smart meter' data.  ...  It provides a study of the effects of aggregation on electrical energy demand modelling and multi-nodal demand forecasting.  ...  Local demand correlations can potentially be exploited to improve the system-level forecast, such as in [21] , where this is achieved by clustering of residential users' smart meter data.  ... 
doi:10.1049/iet-gtd.2017.1599 fatcat:vmljumr625beppftrtzjol6pve

Management of Smart Grids: A Review

Kshitij Singh
2021 International Journal for Research in Applied Science and Engineering Technology  
The demand of 24x7x365 power supply, and development in socio-economic status of individuals and nations the demand of electrical power hence grid complexity is rising.  ...  The present work is an approach addressing various modalities, modes of operation and management of smart grids.  ...  [19] analyzed the multi-year meter data to forecast the load demand in smart grid using data analytics.  ... 
doi:10.22214/ijraset.2021.36734 fatcat:dj3g64cihraihbsijsvojgx56y

An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Anam-Nawaz Khan, Naeem Iqbal, Atif Rizwan, Rashid Ahmad, Do-Hyeun Kim
2021 Energies  
First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers.  ...  Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial  ...  We acquired time-series electric consumption data acquired from smart meters installed in multifamily and multi-storied residential buildings.  ... 
doi:10.3390/en14113020 fatcat:a5bj4gvcvvb7vedq33giyzau3a

Lessons Learnt from Mining Meter Data of Residential Consumers

Konstantinos Blazakis, Sima Davarzani, Georgios Stavrakakis, Ioana Pisica
2016 Periodica Polytechnica Electrical Engineering and Computer Science  
Moreover, with the information extracted from smart meters, the power network would be able to cluster electricity consumers with monitoring their energy usages and data mining in their load profiles patterns  ...  By employing data mining techniques on smart meter recordings, the suppliers can efficiently investigate the load patterns of consumers.  ...  The method used for clustering is based on Euclidean distance by K-means algorithm. The results are obtained for 8 clusters which are shown in Fig. 7 .  ... 
doi:10.3311/ppee.9993 fatcat:ocqbpww5vvdobldpbuyltl66ka

Smart-meter big data for load forecasting: An alternative approach to clustering

Negin Alemazkoor, Mazdak Tootkaboni, Roshanak Nateghi, Arghavan Louhghalam
2022 IEEE Access  
The cluster-based predictions are then aggregated to compute the total demand.  ...  Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters  ...  ACKNOWLEDGMENT The authors would like to thank Minsoo Choi for his help with visualizing the service area map.  ... 
doi:10.1109/access.2022.3142680 fatcat:lb5qpesrkjdgrnmuv6bmia3moe

Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China

Xin Fu, Xiao-Jun Zeng, Pengpeng Feng, Xiuwen Cai
2018 Energy  
Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China. Energy, 165(B), 76-89.  ...  load forecasting, and the predicted load demands of different clusters are aggregated to derive the total usage.  ...  ., hourly or subhourly) smart meter data from residential consumers.  ... 
doi:10.1016/j.energy.2018.09.156 fatcat:4badkcsl5nd43obs3wn7pqbusi
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