Social media data as a proxy for hourly fine-scale electric power consumption estimation

Deng, Chengbin; Lin, Weiying; Ye, Xinyue; Li, Zhenlong; Zhang, Ziang; Xu, Ganggang

doi:10.1177/0308518x18786250

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…At the intracity scale, Liu et al (2018a, b) assessed the utility efficiency of subway stations in a Chinese city by matching the capacity of train services and the travel needs using social media data. Deng et al (2018) analyzed how geotagged tweets are associated with hourly electric consumption at the building level, given the assumption that tweeting behavior is highly related to human activities.…”

Section: Decision Makingmentioning

confidence: 99%

Social Media and Social Awareness

Zhao

Nguyen

et al. 2019

Manual of Digital Earth

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The human behaviors and interactions on social media have maintained themselves as highly dynamic real-time social systems representing individual social awareness at fine spatial, temporal, and digital resolutions. In this chapter, we introduce the opportunities and challenges that human dynamics-centered social media bring to Digital Earth. We review the information diffusion of social media, the multi-faced implications of social media, and some real-world cases. Social media, on one hand, has facilitated the prediction of human dynamics in a wide spectrum of aspects, including public health, emergency response, decision making, and social equity promotion, and will also bring unintended challenges for Digital Earth, such as rumors and location spoofing on the other. Considering the multifaceted implications, this chapter calls for GIScientists to raise their awareness of the complex impacts of social media, to model the geographies of social media, and to understand ourselves as a unique species living both on the Earth and in Digital Earth. Keywords Social media • Human dynamics • Social awareness • Location spoofing 12.1 Introduction: Electronic Footprints on Digital Earth Geo-positioning system-enabled instruments can record and reveal personal awareness at fine spatial, temporal, and digital resolutions (Siła-Nowicka et al. 2016; Li et al. 2017; Ye and Liu 2019). With an exponential growth, human dynamics data are retrieved from location-aware devices, leading to a revolutionary research agenda regarding what happens where and when in the everyday lives of people in both real and virtual worlds (Batty 2013; Yao et al. 2019). Many location-based social media

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Section: Decision Makingmentioning

confidence: 99%

Social Media and Social Awareness

Zhao

Nguyen

et al. 2019

Manual of Digital Earth

Self Cite

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“…It also corroborates the reports of previous studies on inadequacy of electricity supply in the country (Babatunde & Shuaibu, 2009; Subair & Oke, 2008). This further buttresses the fact that Twitter data could be used to predict user behaviors off-line (Butgereit, 2015; Corbett et al, 2018; Deng et al, 2018; Luna et al, 2016).…”

Section: Discussion Of Findingsmentioning

confidence: 97%

Twitter-Mediated Enterprise–Customer Communication: Case of Electricity Distribution Services in a Developing Country

Opesade

2021

Social Science Computer Review

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Studies have shown that electric power supply failures can induce customers’ use of media for electric power–related communications. Nigeria is a country with considerably active users of social media but also with incessant electric power outages. However, no known study has been carried out on Nigeria’s electric power–related communications based on social media data. The present study investigated comparatively, the behaviors of companies and customers, in their use of Twitter for enterprise–customer communication on electric power distribution services in Nigeria. Using the data-driven science methods, the study revealed that both companies and customers use Twitter to disseminate information on electric power distribution in Nigeria. Companies’ corpora feature higher percentages of retweets while customers’ corpora feature higher percentages of direct public responses (@replies). The study also revealed a disjoint in the expectations of the companies and customers in their use of Twitter for communicating electric power distribution matters. While companies appear to leverage on the information sharing ability of the medium, customers appear to perceive it as a tool for accessing improved service delivery. The study recommends that Nigeria’s electric power distribution companies should incorporate Twitter into the customer service operation of their companies. This will enable information to get to the set of people who will process customers’ complaints as soon as possible.

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“…This has been used in the context of building simulation, where geometry and orientation was considered [54,57,77,78] or planning of rural [56] or urban [79,80] grid infrastructure. In other cases regional loads and trends in spatial energy consumption were analyzed [21,[81][82][83] or modelled using socioeconomic data [84][85][86].…”

Section: Techniques and Input Datamentioning

confidence: 99%

“…Historic energy demand [17,27,30,[61][62][63]75,[84][85][86]91,96,118,131,133,134,138,140,143,146,[148][149][150]152,154,[156][157][158][159][160]168,181,189,192,202,230,231,234,239,240,242,243,255,262,263,268,270,273,277,280,282,…”

Section: Regressionmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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Social media data as a proxy for hourly fine-scale electric power consumption estimation

Cited by 11 publications

References 5 publications

Social Media and Social Awareness

Social Media and Social Awareness

Twitter-Mediated Enterprise–Customer Communication: Case of Electricity Distribution Services in a Developing Country

Modeling Energy Demand—A Systematic Literature Review

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