Energy load forecast using S2S deep neural networks with k-Shape clustering

Jarábek, Tomáš; Laurinec, Peter; Lucká, Mária

doi:10.1109/informatics.2017.8327236

Cited by 19 publications

(10 citation statements)

References 6 publications

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“…It employs a deep embedded clustering which is able to extract the new features and forms the clusters jointly. A combination of deep neural networks and K-shape clustering is used in [108] for load forecasting. Ryu et al [109] propose a joint deep learning and clustering process that captures daily and seasonal variations.…”

Section: Future Trendsmentioning

confidence: 99%

A comparative study of clustering techniques for electrical load pattern segmentation

Rajabi

Eskandari

Ghadi

et al. 2020

Renewable and Sustainable Energy Reviews

129

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Section: Future Trendsmentioning

confidence: 99%

A comparative study of clustering techniques for electrical load pattern segmentation

Rajabi

Eskandari

Ghadi

et al. 2020

Renewable and Sustainable Energy Reviews

129

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“…Further, the household energy demand is much more volatile than an aggregated load of multiple households, meaning researchers need to consider other external inputs such as occupancy behavior, building characteristics, and even income and employment status (Ramokone et al, 2020;Yuce et al, 2017). Regarding grid control, researchers leverage clustering algorithms on households before training their models to ensure that volatile patterns do not become an issue and increase accuracy (Aurangzeb et al, 2021;Jarábek et al, 2018;Khan & Jayaweera, 2018).…”

Section: Resultsmentioning

confidence: 99%

A scoping review of deep neural networks for electric load forecasting

Vanting

Jôrgensen

2021

Energy Inform

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The increasing dependency on electricity and demand for renewable energy sources means that distributed system operators face new challenges in their grid. Accurate forecasts of electric load can solve these challenges. In recent years deep neural networks have become increasingly popular in research, and researchers have carried out many experiments to create the most accurate deep learning models. Players in the energy sector can exploit the increasing amount of energy-related data collected from smart meters to improve the grid’s operating quality. This review investigates state-of-the-art methodologies relating to energy load forecasting using deep neural networks. A thorough literature search is conducted, which outlines and analyses essential aspects regarding deep learning load forecasts in the energy domain. The literature suggests two main perspectives: demand-side management and grid control on the supply side. Each perspective has multiple applications with its challenges to achieve accurate forecasts; households, buildings, and grids. This paper recommends using a hybrid deep learning multivariate model consisting of a convolutional and recurrent neural network based on the scoping review. The suggested input variables should be historical consumption, weather, and day features. Combining the convolutional and recurrent networks ensures that the model learns as many repeating patterns and features in the data as possible.

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“…In this study, the K-shape algorithm developed by Paparrizos and Gravano ( 38 ) is used, which proposes a normalized version of the cross-correlation measure. It has been verified to perform well in creating homogeneous and well-separated clusters ( 39 ). The theory of the K-shape algorithm is introduced as follows.…”

Section: Methodsmentioning

confidence: 99%

Exploring Taxi Demand Distribution of Comprehensive Land-Use Patterns Using Online Car-Hailing Data and Points of Interest in Chengdu, China

Wang

Hua

et al. 2021

Transportation Research Record

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With the rapid advance of urbanization, land-use intensity is increasing, and various land-use forms gather to form comprehensive land-use patterns. Traffic demand shows variability and complexity under comprehensive land-use patterns. Accurate analysis of traffic demand in urban transportation is the key to active traffic control and road guidance. Researchers have widely studied the relationship between traffic demand and land-use patterns, while land-use intensity is ignored when classifying land-use patterns, and the traffic demand distribution in each land-use pattern is not studied specifically. Taxi is a flexible public mode in urban areas, and taxi demand is an important component in analyzing traffic demand and identifying traffic hotspots in cities. This paper explores taxi demand distribution of comprehensive land-use patterns using online car-hailing data and points of interest (POI) in Chengdu, China. The demand-driven traffic analysis zones are developed by clustering origin–destination points of online car-hailing services. Using POI data, comprehensive land-use patterns are classified with land-use forms and land-use intensity. The K-shape algorithm is adopted to extract the typical taxi demand distribution in each comprehensive land-use pattern. Finally, two indicators, total taxi demand (TTD) and taxi demand difference (TDD), are computed and further analyzed. Results show that taxi demand distribution is still differential even under the same land-use pattern. Three land-use patterns whose average hourly taxi demand reaches about 300 vehicles per square kilometer have the largest TTD and most uneven TDD. The findings can support traffic management, land-use combination, and land-use adjustment to avoid concentrated taxi demand and mismatched TDD.

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Energy load forecast using S2S deep neural networks with k-Shape clustering

Cited by 19 publications

References 6 publications

A comparative study of clustering techniques for electrical load pattern segmentation

A comparative study of clustering techniques for electrical load pattern segmentation

A scoping review of deep neural networks for electric load forecasting

Exploring Taxi Demand Distribution of Comprehensive Land-Use Patterns Using Online Car-Hailing Data and Points of Interest in Chengdu, China

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