Spatio-Temporal Analysis and Forecasting of Distributed PV Systems Diffusion: A Case Study of Shanghai Using a Data-Driven Approach

Zhao, Teng; Zhou, Zijing; Zhang, Yan; Ling, Ping; Tian, Yingjie

doi:10.1109/access.2017.2694009

Cited by 23 publications

(9 citation statements)

References 37 publications

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“…Data analysis with spatial resolution high enough to address these issues is rare due to poor data availability. To the best of our knowledge, there have been only a few exceptions: a study by Müller and Rode (2013), who investigated Wiesbaden City, and later extended their target area to all of Germany (Rode and Weber 2016); and, studies focused on Connecticut in the US (Graziano and Gillingham 2015), and Shanghai, China (Zhao et al 2017). These studies successfully conducted analyses at the smaller district level by referring to the position coordinates of individual system installations.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Neighborhood influences on the diffusion of residential photovoltaic systems in Kyoto City, Japan

Kosugi

Shimoda

Tashiro³

2019

Environ Econ Policy Stud

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This study investigates the factors influencing the diffusion of residential photovoltaic systems. Factors examined are related to social attributes, such as population structure and living environment within neighborhoods and those close by, together with a neighbor effect revealed as a positive spatial dependency of the diffusion. To examine these factors simultaneously, the study applies a spatial econometric analysis, taking advantage of the availability of cumulative data on installed residential photovoltaic systems and census-based social attributes in about 4000 census blocks in Kyoto City, which include 1.47 million people. Results include: (1) an observed neighbor effect, especially between census blocks within a radius of 1000 m; (2) evidence that diffusion is positively influenced in a census block by lower population density and higher number of household members, as well as by lower ratios of detached houses and lower population densities in nearby census blocks; and (3) indication that diffusion is positively influenced by a higher proportion of young people through various mechanisms. To further facilitate the diffusion, implementing non-economic measures designed in light of the observed neighborhood influences is recommended, in addition to conventional economic support measures.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Neighborhood influences on the diffusion of residential photovoltaic systems in Kyoto City, Japan

Kosugi

Shimoda

Tashiro³

2019

Environ Econ Policy Stud

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“…In [60] agents are modeled as single buildings and in [52] agents are modeled as households. W.r.t [50], [53], [54], [54]- [59], [61], [62] we integrate fine-grained GIS data and perform an analysis in a real district with a resolution up to the household level. Moreover, w.r.t [52], [60] we modeled both the single household and the condominium adoption behavior.…”

Section: Comparative Discussion and Our Contributionmentioning

confidence: 99%

“…Finally, considering methods slightly oriented towards agent-based approach, Cellular Automation requires a grid lattice to make decisions based on neighborhoods of some sort, so even a square or hexagonal lattice. For instance, Zhou et al [49] and Zhao et al [50] developed a data-driven forecasting approach of PV diffusion in Pudong district of Shanghai, China by using a Cellular Automation model based on artificial neural network, where the study region was divided into square cells.…”

Section: A Non-abm Approachesmentioning

confidence: 99%

Analysis of Rooftop Photovoltaics Diffusion in Energy Community Buildings by a Novel GIS- and Agent-Based Modeling Co-Simulation Platform

et al. 2019

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The present work introduces an empirically ground agent-based modeling (ABM) framework to assess the spatial and temporal diffusion of rooftop photovoltaic (PV) systems on existing buildings of a city district. The overall ABM framework takes into account social, technical, environmental, and economic aspects to evaluate the diffusion of PV technology in the urban context. A city district that includes 18 720 households distributed over 1 290 building blocks and a surface area of 2.47 km2 is used to test the proposed ABM framework. Results show how the underlying regulatory framework (i.e., the rules of the internal electricity market) influences the pattern and intensity of adoption, thus realizing different shares of the available potential. Policies that support the establishment of 'prosumers' within Condominiums (i.e., energy community buildings), and not in single-family houses only, is key to yield high diffusion rates. The installed capacity increases by 80% by switching from the one-to-one configuration to the one-to-many paradigm, i.e., from 5.90 MW of rooftop PV installed on single-family households and/or single PV owners to 10.64 MW in energy community buildings. Moreover, the possibility to spread the auto-generated solar electricity over the load profile of the entire population of Condominium results in self-consumption rates greater than 50% and self-sufficiency ratios above 20% for the majority of the simulated buildings. INDEX TERMS Agent-based modeling, complex adaptive systems, consumer behavior, distributed power generation, energy technology diffusion, geospatial analysis, photovoltaic systems, public policy, sustainable development, technology adoption, urban areas.

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“…Reference (Khodayar et al, 2017) studies the ultra-short-term wind forecasting with the deep learning method through unsupervised feature learning from the unlabeled historical wind speed data. The forecasting approach of distributed solar energy systems from macro-and micro-aspects is discussed in a general way in (ZHAO et al, 2017) with clustering of capacity and location of PV system. The data-driven forecasting approach of PV diffusion is proposed based on cellular automation in microscopic analysis.…”

Section: Renewable Energy Forecastingmentioning

confidence: 99%

Big data analytics in smart grids: a review

2018

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Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.

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Spatio-Temporal Analysis and Forecasting of Distributed PV Systems Diffusion: A Case Study of Shanghai Using a Data-Driven Approach

Cited by 23 publications

References 37 publications

Neighborhood influences on the diffusion of residential photovoltaic systems in Kyoto City, Japan

Neighborhood influences on the diffusion of residential photovoltaic systems in Kyoto City, Japan

Analysis of Rooftop Photovoltaics Diffusion in Energy Community Buildings by a Novel GIS- and Agent-Based Modeling Co-Simulation Platform

Big data analytics in smart grids: a review

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