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

Wijaya, Tri Kurniawan; Vasirani, Matteo; Humeau, Samuel; Aberer, Karl

doi:10.1109/bigdata.2015.7363836

Cited by 59 publications

(43 citation statements)

References 32 publications

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“…Therefore, with the insertion of DRES, the quality of tension cannot be guaranteed if there is not a communications system to provide timely information of the state of the conventional network. To ensure the quality of tension in the network, through the integration of microgrids, the tension levels of the conventional network and the DRES must be resynchronized [78]. This resynchronization can be done by obtaining real time information of the state of the network.…”

Section: Ami In Microgridsmentioning

confidence: 99%

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Inga¹,

Campaña²,

Hincapié³

et al. 2018

Preprint

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The unpredictable increase in electrical demand affects the quality of theenergy throughout the network. A solution to the problem is the increase of distributedgeneration units which burn fossil fuels.While this is an immediate solution to theproblem the ecosystem gets affected by the emission of CO2.A promising solutionis the integration of Distributed Renewable Energy Sources (DRES) to the conventionalelectrical system, thus, introducing the concept of smart microgrids (SMG) that requirea safe, reliable and technically planned two-way communication system. This documentpresents a heuristic based on planning capable of providing a bidirectional communicationnear optimal route map, following the structure of an hybrid Fiber-Wireless (FiWi) with thepurpose of obtaining information of electrical parameters that help us to manage the useof energy by integrating conventional electrical system to SMG. A FiWi network is basedon the integration of wireless access and optical networks. This integration increases thecoverage and reliability at a lower cost. The optimization model is based on clusteringtechniques, through the construction of balanced conglomerates. The method is used forthe development of the clusters along with the Nearest-Neighbor Spanning Tree Algorithm(N-NST). Additionally, Optimal Delay Balancing (ODB) model will be used to minimizethe end to end delay of each grouping. In addition, the heuristic observes real designparameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes arebuilt following the minimum shorter path. Therefore, this paper presents a heuristic able to plan the deployment of smart meters (SMs) through a tree-like hierarchical topology for theintegration of SMG at the lowest cost.

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Section: Ami In Microgridsmentioning

confidence: 99%

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Inga¹,

Campaña²,

Hincapié³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Some research has used the clustering results to improve the load forecasting. In [42] the authors aim to forecast household loads for two time periods: one hour and 24 hour ahead forecasting. They propose a new method called -cluster-based aggregate forecasting (CBAF)‖ and compare it with two other approaches, i.e., (1) to aggregate the energy consumption of all households into one time series (the aggregate consumption), then forecast the aggregate consumption, and (2) to forecast the energy consumption of each household separately, then aggregate the forecasts.…”

Section: B Load Forecastingmentioning

confidence: 99%

A review on clustering of residential electricity customers and its applications

Rajabi

Zhang

et al. 2017

2017 20th International Conference on Electrical Machines and Systems (ICEMS)

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Clustering is a well-recognized data mining technique which enables the determination of underlying patterns in datasets. In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting. Some surveys summarized different clustering techniques which were traditionally used for customer segmentation and load profiling. The recent changes in power system structure and introduction of new technologies necessitate the new investigation of applications and benefits of clustering methods for power systems. In this regard, this paper aims at reviewing the new research for clustering techniques for residential customers.

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“…The reason is that the MAPE may become unreliable when the consumption data are small or close to zero, which is common in small scales; whereas the NRMSE is not affected by such problem [11].…”

Section: Model Evaluationmentioning

confidence: 99%

“…The ANN has been proved to be an effective tool in STLF, particularly in small scales [10]. Although the SVR has good performance in large scales, it is outperformed by simple linear models in small scales [11].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Load Forecasting of Active and Reactive Powers in Small Scales using LASSO-Integrated Nonlinear Autoregressive Neural Networks

Zhang¹

2016

Preprint

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Abstract-Short-term load forecasting (STLF) is important for power system planning and optimization, especially in the dynamic environment of smart grid. Traditional load forecasting is implemented at substation levels to predict the upcoming active power and optimal system settings. In more advanced smart grid applications, e.g. the Volt-VAR Control, small-scale load forecasting opens up new opportunities in coordinating distributed resources such as distributed generation (DG) with utilities' efficiency missions. This paper proposes a STLF approach for small residential blocks with 10-12 households. The Nonlinear Autoregressive Neural Network (NAR-NN) is employed to predict hour-ahead active (P) and reactive (Q) powers with a moving window of training data. The regressor shrinkage technique, LASSO, is used to improve the selection of the regressors in the NAR-NN model by removing insignificant input features. The results show the forecasting performance could be enhanced by 20% comparing to feed-forward Artificial Neural Networks (ANNs). The improvement in forecasting both P & Q could accommodate new smart grid applications in small scales.Index Terms-Short-term load forecasting, nonlinear autoregressive neural network, regression shrinkage, power system planning, smart grid.

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

Cited by 59 publications

References 32 publications

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

A review on clustering of residential electricity customers and its applications

Short-Term Load Forecasting of Active and Reactive Powers in Small Scales using LASSO-Integrated Nonlinear Autoregressive Neural Networks

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