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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一薰(I-Hsuan Hong) | |
| dc.contributor.author | Wen-Ting Chang | en |
| dc.contributor.author | 張雯婷 | zh_TW |
| dc.date.accessioned | 2021-06-08T00:06:43Z | - |
| dc.date.copyright | 2020-08-13 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-06 | |
| dc.identifier.citation | 中央氣象局. (2020). 自動氣象站氣象觀測資料. 取自https://opendata.cwb.gov.tw/dataset/observation/O-A0001-001. 行政院. (2020). 空氣污染防制行動方案. 取自https://www.ey.gov.tw/Goals/81689F916EB5D550. 交通部公路總局. (2020). 車輛概況. 取自https://stat.thb.gov.tw/hb01/webMain.aspx?sys=100 funid=11100. 車輛測試研究中心. (2015). 台灣智慧電動車推廣現況與政策方向.取自https://www.artc.org.tw/upfiles/ADUpload/knowledge/tw_knowledge_491501630.pdf. 國際能源署. (2018). Global ev outlook. 陳錦嫣及黃國展. (2013). Arcgis 地理資訊系統入門與應用. 新文京. 智能電動車輛產業輔導推廣計畫. (2020). 全台充電站查詢. 取自https://www.lev.org.tw/iev/location_C.aspx?id=27. 臺北市政府主計處. (2018). 第1014 號市政統計週報. 取自https://dbas.gov.taipei/News.aspx?n=1A92853853322CF6 sms=AC3003A760CEDBAC. 臺北市政府民政局. (2018). 臺北市每年人口數依性別及年齡分(按行政區、里別). 取自https://ca.gov.taipei/News_Content.aspx?n=8693DC9620A1AABF sms=D19E9582624D83CB s=78DC4B104D9D374E. 臺北市資料大平台. (2020). 道路速率. 取自https://tcgbusfs.blob.core.windows.net/blobtisv/GetVD.xml.gz. 臺北市停車管理工程處. (2020). 臺北市停車資訊導引系統. 取自http://tpis.pma.gov.tw/ParkInfo/pda. Abramowitz, M., Stegun, I. A. (1972). Handbook of mathematical functions with formulas. Wiley. Acello, R. (1997). Getting into gear with the vehicle of the future. San Diego Business Journal, 18, 15. Adepeju, M., Rosser, G., Cheng, T. (2016). Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions a crime case study. International Journal of Geographical Information Science, 30(11), 21332154. Arias, M. B., Bae, S. (2016). Electric vehicle charging demand forecasting model based on big data technologies. Applied Energy, 183, 327 339. Arias, M. B., Kim, M., Bae, S. (2017). Prediction of electric vehicle charging-power demand in realistic urban traffic networks. Applied Energy, 195, 738 753. Baddeley, A., Rubak, E., Turner, R. (2015). Spatial point patterns : Methodology and applications with r. Barbecho Bautista, P., Lemus Cárdenas, L., Urquiza Aguiar, L., Aguilar Igartua, M. (2019). A trafficaware electric vehicle charging management system for smart cities. Vehicular Communications, 20, 100188. Blangiardo, M., Cameletti, M. (2015). Spatial and spatio-temporal bayesian models with r inla. Wiley. Bonges, H. A., Lusk, A. C. (2016). Addressing electric vehicle (ev) sales and range anxiety through parking layout, policy and regulation. Transportation Research Part A: Policy and Practice, 83, 63 73. CarpioPinedo, J. (2014). Urban bus demand forecast at stop level: Space syntax and other built environment factors. evidence from madrid. Procedia Social and Behavioral Sciences, 160, 205 214. Chakraborty, D., Bunch, D. S., Lee, J. H., Tal, G. (2019). Demand drivers for charging infrastructure-charging behavior of plug-in electric vehicle commuters. Transportation Research Part D: Transport and Environment, 76, 255 272. Cowpertwait, P. S. (2006). A spatial–temporal point process model of rainfall for the thames catchment, uk. Journal of Hydrology, 330(3), 586 595. Cox, D. R. (1955). Some statistical methods connected with series of events. Journal of the Royal Statistical Society. Series B (Methodological), 17(2), 129–164. Cronie, O., Lieshout, M. N. M. V. (2015). A j-function for inhomogeneous spatio-temporal point processes. Scandinavian Journal of Statistics, 42(2), 562579. Daley, D. J., VereJones, D. (1988). An introduction to the theory of point processes. Daley, D. J., VereJones, D. (2003). An introduction to the theory of point processes. Diggle, P. J., Chetwynd, A., Häggkvist, R., Morris, S. (1995). Second-order analysis of spacetime clustering. Statistical Methods in Medical Research, 4(2), 124136. Diggle, P. J., Moraga, P., Rowlingson, B., Taylor, B. M. (2013). Spatial and spatio-temporal log-gaussian cox processes: Extending the geostatistical paradigm. Statistical Science, 28(4), 542–563. Dong, G., Ma, J., Wei, R., Haycox, J. (2019). Electric vehicle charging point placement optimisation by exploiting spatial statistics and maximal coverage location models. Transportation Research Part D: Transport and Environment, 67, 77 88. Elbanhawy, E., Dalton, R., Nassar, K. (2013, 04). Integrating space-syntax and discrete-event simulation for e-mobility analysis. In (p. 934945). Flammini, M. G., Prettico, G., Julea, A., Fulli, G., Mazza, A., Chicco, G. (2019). Statistical characterisation of the real transaction data gathered from electric vehicle charging stations. Electric Power Systems Research, 166, 136 150. Gabriel, E., Diggle, P. J. (2009). Second-order analysis of inhomogeneous spatiotemporal point process data. Statistica Neerlandica, 63(1), 4351. Gabriel, E., Rowlingson, B., Diggle, P. J. (2013). stpp: An r package for plotting, simulating and analysing spatio-temporal point patterns. Journal of Statistical Software, 53. Gelman, A., Hwang, J., Vehtari, A. (2014). Understanding predictive information criteria for bayesian models. Statistics and Computing, 24, 997–1016. GomezRubio, V. (2020). Bayesian inference with inla. CRC Press. Guerra, E., Daziano, R. A. (2020). Electric vehicles and residential parking in an urban environment: Results from a stated preference experiment. Transportation Research Part D: Transport and Environment, 79, 102222. Hardman, S., Jenn, A., Tal, G., Axsen, J., Beard, G., Daina, N., … Witkamp, B. (2018). A review of consumer preferences of and interactions with electric vehicle charging infrastructure. Transportation Research Part D: Transport and Environment, 62, 508 523. Hillier, B., Hanson, J. (1984). The social logic of space. Cambridge University Press. Hu, L., Dong, J., Lin, Z. (2019). Modeling charging behavior of battery electric vehicle drivers: A cumulative prospect theory based approach. Transportation Research Part C: Emerging Technologies, 102, 474 489. Iftimi, A., van Lieshout, M.C., Montes, F. (2018). A multi-scale area-interaction model for spatio-temporal point patterns. Spatial Statistics, 26, 38 55. Illian, J., Penttinen, A., Stoyan, H., Stoyan, D. (2008). Statistical analysis and modelling of spatial point patterns. Illian, J. B., Martino, S., Sørbye, S. H., GallegoFernández, J. B., Zunzunegui, M., Esquivias, M. P., Travis, J. M. J. (2013). Fitting complex ecological point process models with integrated nested laplace approximation. Methods in Ecology and Evolution, 4(4), 305315. Lan, S. (2019). Learning temporal evolution of spatial dependence with generalized spatiotemporal gaussian process models. Li, X., Chen, P., Wang, X. (2017). Impacts of renewables and socioeconomic factors on electric vehicle demands–panel data studies across 14 countries. Energy Policy, 109, 473 478. Lin, H., Fu, K., Wang, Y., Sun, Q., Li, H., Hu, Y., … Wennersten, R. (2019). Characteristics of electric vehicle charging demand at multiple types of location application of an agent-based trip chain model. Energy, 188, 116122. Liu, Z., Song, Z. (2018). Network user equilibrium of battery electric vehicles considering flow-dependent electricity consumption. Transportation Research Part C: Emerging Technologies, 95, 516 544. Minasny, B., McBratney, A. B. (2005). The matérn function as a general model for soil variograms. Geoderma, 128(3), 192 207. Minasny, B., McBratney, A. B. (2007). Spatial prediction of soil properties using eblup with the matérn covariance function. Geoderma, 140(4), 324 336. Moller, J., Syversveen, A. R., Waagepetersen, R. P. (1998). Log gaussian cox processes. Scandinavian Journal of Statistics, 25(3), 451–482. Moller, J., Waagepetersen, R. P. (2004, 01). Statistical inference and simulation for spatial point process. , 100. Morton, C., Anable, J., Yeboah, G., Cottrill, C. (2018). The spatial pattern of demand in the early market for electric vehicles: Evidence from the united kingdom. Journal of Transport Geography, 72, 119 130. Niu, M., Dai, Z., Lawrence, N., Becker, K. (2016, 10). Spatio-temporal gaussian processes modeling of dynamical systems in systems biology. arXiv: Machine Learning. Palanca, J., Jordán, J., Bajo, J., Botti, V. (2020). An energy-aware algorithm for electric vehicle infrastructures in smart cities. Future Generation Computer Systems, 108, 454 466. Plummer, M. (2008). Penalized loss functions for bayesian model comparison. Biostatistics, 9, 523539. Rasmussen, C. E., Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press. Riebler, A., Held, L. (2010). The analysis of heterogeneous time trends in multivariate age-period-cohort models. Biostatistics, 11, 5769. Rostami, M., Mohammadi, Y., Jalilian, A., Nazparvar, B. (2017). Modeling spatio-temporal variations of substance abuse mortality in iran using a log-gaussian cox point process. Spatial and Spatio-temporal Epidemiology, 22, 15 25. Rue, H., Fong, Y., Wakefield, J. (2010). Bayesian inference for generalized linear mixed models. Biostatistics, 11(3), 397412. SAE International. (2012). Sae electric vehicle and plug in hybrid electric vehicle conductive charge coupler. https://www.sae.org/standards/content/j1772_201210/. Sarkka, S., Solin, A., Hartikainen, J. (2013). Spatiotemporal learning via infinite-dimensional bayesian filtering and smoothing: A look at gaussian process regression through kalman filtering. IEEE Signal Processing Magazine, 30(4), 5161. Schoenberg, F. P., Brillinger, D. R., Guttorp, P. (2006). Point processes, spatial–temporal. Encyclopedia of Environmetrics, 3(11), 1573–1577. Shepero, M., Munkhammar, J. (2018). Spatial markov chain model for electric vehicle charging in cities using geographical information system (gis) data. Applied Energy, 231, 1089 1099. Vehtari, A., Gelman, A., Gabry, J. (2017). Practical bayesian model evaluation using leave-one-out cross-validation and waic. Statistics and Computing, 27, 1413–1432. Wagner, S., Goetzinger, M., Neumann, D. (2013). Optimal location of charging stations in smart cities: A points of interest based approach. International Conference on Information Systems (ICIS 2013): Reshaping Society Through Information Systems Design, 3, 2838 2855. Wang, D., Gao, J., Li, P., Wang, B., Zhang, C., Saxena, S. (2017). Modeling of plugin electric vehicle travel patterns and charging load based on trip chain generation. Journal of Power Sources, 359, 468 479. WANG, X., YUE, Y. R., FARAWAY, J. J. (2020). Bayesian regression modeling with inla. CRC PRESS. Wang, Y., Infield, D. (2018). Markov chain monte carlo simulation of electric vehicle use for network integration studies. International Journal of Electrical Power Energy Systems, 99, 85 94. Wang, Z., Jochem, P., Fichtner, W. (2020). A scenario-based stochastic optimization model for charging scheduling of electric vehicles under uncertainties of vehicle availability and charging demand. Journal of Cleaner Production, 254, 119886. Webster, R., Oliver, M. A. (2009). Geostatistics for environmental scientists. Wolbertus, R., Kroesen, M., van den Hoed, R., Chorus, C. (2018). Fully charged: An empirical study into the factors that influence connection times at ev-charging stations. Energy Policy, 123, 1 7. Xiana, H., lipeng, Z. (2017). Simulation of pedestrian flow in traditional commercial streets based on space syntax. Procedia Engineering, 205, 1344 1349. Xiufan, M., Ying, L., Hao, W., Chao, W., Xiao, H. (2017). Research on demand of charging piles based on stochastic simulation of ev trip chain. Transactions of China Electrotechnical Society, 190 202. Yang, J., Wu, F., Yan, J., Lin, Y., Zhan, X., Chen, L., … Sun, Y. (2020). Charging demand analysis framework for electric vehicles considering the bounded rationality behavior of users. International Journal of Electrical Power Energy Systems, 119, 105952. Yang, Y., Yao, E., Yang, Z., Zhang, R. (2016). Modeling the charging and route choice behavior of bev drivers. Transportation Research Part C: Emerging Technologies, 65, 190 204. Zhou, Z., Matteson, D. S., Woodard, D. B., Henderson, S. G., Micheas, A. C. (2015). A spatio-temporal point process model for ambulance demand. Journal of the American Statistical Association, 110(509), 615. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17321 | - |
| dc.description.abstract | 作為減緩氣候變遷的手段之一為讓電動汽車取代傳統燃油車，然而鑑於電動汽車公共充電站的覆蓋率不足導致消費者購買電動汽車的意願降低，因此唯有先了解電動汽車的充電需求才能在資源有限的情況下妥善地規劃充電站。本論文利用時空點過程的過程強度（intensity function）描述電動汽車充電的潛在需求，建構適合的時空點過程模型以及時空高斯過程模型，並找出影響電動汽車充電需求的因素。在案例研究中，我們配適了兩種解釋模型：時空點過程中的對數高斯 Cox 點過程（log Gaussian Cox process）以及時空高斯過程模型（spatio-temporal Gaussian process）來描述台北市電動汽車充電事件點的分布情形，並根據我們的研究結果顯示最適切於台北市電動汽車充電事件點模式之模型以時空高斯過程模型較佳，未來可運用該模型來預測台北市電動汽車充電需求的潛在時空分佈。 | zh_TW |
| dc.description.abstract | One of obstacles to replacing fossil fuel cars with electric vehicles is the lack of charging facilities. In order to set up charging facilities appropriately with limited resources, the charging demand needs to be analyzed and understood. This thesis uses the intensity function of the spatio-temporal point process to describe the potential electric vehicle charging demand. We construct a suitable spatio-temporal model and find out the factors that would affect the electric vehicle charging demand. In this study, we examine two explanatory models: log Gaussian Cox point process and spatio-temporal Gaussian process. In our case study, we find that the most suitable model for the electric vehicle charging event point model in Taipei is the spatio-temporal Gaussian process model. This model could predict the potential spatial and temporal distribution of the electric vehicle charging demand in Taipei in the future. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T00:06:43Z (GMT). No. of bitstreams: 1 U0001-0608202011294900.pdf: 3731996 bytes, checksum: 9c49a17d45cc041b05d7cd471c63553f (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 i 摘要 ii Abstract iii 第一章緒論 1 第二章時空過程模型 6 2.1 時空點過程模型 6 2.1.1 空間點過程的類型 7 2.1.2 時空點過程的基本性質 8 2.1.3 時空非均勻K-函數 10 2.1.4 非均勻卜瓦松過程 11 2.1.5 Gibbs 過程 12 2.1.6 對數高斯Cox 過程 13 2.1.7 參數估計 14 2.2 時空高斯過程模型 15 2.2.1 高斯過程 16 2.2.2 時空高斯過程 19 2.3 模型設定 19 2.3.1 時空點過程模型設定 19 2.3.2 時空高斯過程模型設定 20 2.4 模型比較的衡量指標 21 2.4.1 廣泛適用的信息準則 21 2.4.2 均方誤差 22 第三章個案研究 23 3.1 研究區域 23 3.2 數據來源與資料清理整合 24 3.2.1 電動汽車歷史充電資料 24 3.2.2 人口數 25 3.2.3 興趣點數 25 3.2.4 交通便捷性 26 3.2.5 交通量 27 3.2.6 氣象變量 28 3.3 資料分析流程 29 3.4 時空相關性分析 29 3.5 模型配適結果 30 3.6 預測結果 33 第四章結論與未來研究 35 參考文獻 36 | |
| dc.language.iso | zh-TW | |
| dc.title | 基於時空過程模型預測電動汽車充電需求 | zh_TW |
| dc.title | Prediction of electric vehicle charging demand based on spatio-temporal process model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭財吉(Tsai-Chi Kuo),鄧志峰(Jr-Fong Dang) | |
| dc.subject.keyword | 電動汽車充電需求,時空模型,對數高斯Cox過程,高斯過程, | zh_TW |
| dc.subject.keyword | Electric vehicle charging demand,Spatio-temporal model,Log Gaussian Cox process,Gaussian process, | en |
| dc.relation.page | 42 | |
| dc.identifier.doi | 10.6342/NTU202002517 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-06 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| 顯示於系所單位： | 工業工程學研究所 | |
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