Forecasting Method of Newly-Connected Customer&s Load Curve on LV distribution by Means of Regression Tree Utilizing Smart Meter Data

2021 ◽  
Author(s):  
Y. Kanazawa ◽  
T. Nonoyama ◽  
K. Yasunami ◽  
T. Takano ◽  
K. Matsuda
2014 ◽  
Vol 134 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Hisatomo Miyata ◽  
Kazutoshi Miyashita ◽  
Takayuki Endo ◽  
Yuichi Shimasaki ◽  
Tatsuya Iizaka ◽  
...  

1973 ◽  
Vol 93 (11) ◽  
pp. 575-581
Author(s):  
E. MIYAMOTO ◽  
T. KOIKE

2018 ◽  
Vol 8 (1) ◽  
pp. 38-50 ◽  
Author(s):  
Peter Laurinec ◽  
Mária Lucká

Abstract This paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of various model-based time series representation methods. Final centroid-based forecasts are scaled by saved normalisation parameters to create the forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on three smart meter datasets from residences of Ireland and Australia, and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy mainly for residential consumers.We can also proclaim that it can be found such time series representation and clustering setting that will our forecasting method perform more accurately than fully disaggregated approach. Our method is also more scalable since it is necessary to train the model only on clusters and not for every consumer separately


Energy ◽  
2017 ◽  
Vol 137 ◽  
pp. 118-128 ◽  
Author(s):  
Esther Villar-Rodriguez ◽  
Javier Del Ser ◽  
Izaskun Oregi ◽  
Miren Nekane Bilbao ◽  
Sergio Gil-Lopez

TAPPI Journal ◽  
2019 ◽  
Vol 18 (3) ◽  
pp. 161-169
Author(s):  
Doug Cash ◽  
Benjamin Frank

The corrugated industry typically quantifies crush resistance using the Concora corrugated medium test (CMT) on fluted medium or flat crush on combined board. These tests compress the materials until the point of complete failure of the flutes. Combined board elastically resists crushing forces until a certain point, the hardness of the structure, while additional load causes permanent damage and deformation. This study investigates how hardness can be measured directly from a load curve collected during CMT (or flat crush) testing and how it varies throughout the North American paper supply. It also explores how hardness correlates with the values obtained from the newly developed S-test. This new test method deserves further study as a potentially more appropriate specification for crush resistance of corrugated medium.


2020 ◽  
Vol 638 ◽  
pp. 149-164
Author(s):  
GM Svendsen ◽  
M Ocampo Reinaldo ◽  
MA Romero ◽  
G Williams ◽  
A Magurran ◽  
...  

With the unprecedented rate of biodiversity change in the world today, understanding how diversity gradients are maintained at mesoscales is a key challenge. Drawing on information provided by 3 comprehensive fishery surveys (conducted in different years but in the same season and with the same sampling design), we used boosted regression tree (BRT) models in order to relate spatial patterns of α-diversity in a demersal fish assemblage to environmental variables in the San Matias Gulf (Patagonia, Argentina). We found that, over a 4 yr period, persistent diversity gradients of species richness and probability of an interspecific encounter (PIE) were shaped by 3 main environmental gradients: bottom depth, connectivity with the open ocean, and proximity to a thermal front. The 2 main patterns we observed were: a monotonic increase in PIE with proximity to fronts, which had a stronger effect at greater depths; and an increase in PIE when closer to the open ocean (a ‘bay effect’ pattern). The originality of this work resides on the identification of high-resolution gradients in local, demersal assemblages driven by static and dynamic environmental gradients in a mesoscale seascape. The maintenance of environmental gradients, specifically those associated with shared resources and connectivity with an open system, may be key to understanding community stability.


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