Energy demand estimation using quasi-real-time people activity data

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Energy Procedia 158 Energy Procedia 00(2019) (2017)4172–4177 000–000 www.elsevier.com/locate/procedia

10th th

International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10 International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China

Energy demand estimation using quasi-real-time people activity data The 15th Internationalusing Symposium on District Heating and Cooling Energy demand estimation quasi-real-time people activity data Takahiro Yoshidaaa*, Yoshiki Yamagataaa, Daisuke Murakamibb Takahiro Yoshida *, Yoshiki Yamagata Murakami Assessing the feasibility of using the, Daisuke heat demand-outdoor a National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan National for Mathematics, Environmental10-3 Studies, 16-2 Onogawa, Tsukuba Institute of Institute Statistiacal Midori-cho, Tachikawa, Tokyo305-8506, 190-8562,Japan Japan b Institute of Statistiacal Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan

temperature function for a long-term district heat demand forecast b a

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel,demand 78520 Limay, France This study proposes an approach to estimate quasi-real-time electricity/energy in each commercial building using c Département Systèmes Énergétiques et Environnement IMT Atlantique, 4 rue Alfred Kastler, 44300 France This study proposes an approach to estimatedata quasi-real-time electricity/energy demand in each commercial building using Google's Populartime data. The Populartime records real-time human locations/activities that areNantes, collected from users of Google's Populartime data. The Populartime data records real-time human locations/activities that are collected users of maps on smartphones. The proposed approach considering changes by hour and by day of the week from is applied to Google's mapsTokyo, on smartphones. proposed approach considering changes by hour and bydemand day of monitoring the week isconsidering applied to Sumida-ward, Japan. TheThe result suggests the usefulness of our approach for energy Sumida-ward, Tokyo, Japan. The result suggests the usefulness of our approach for energy demand monitoring considering quasi-real-time human activities. Abstract quasi-real-time human activities. Copyright © 2018networks Elsevier Ltd. All rights reserved. heating are commonly addressed in the literature as one of the most effective solutions for decreasing the ©District 2019 The Published by Elsevier Ltd. Copyright ©Authors. 2018 Elsevier Ltd. Allresponsibility rights reserved. Selection and peer-review under of the scientific committee of investments the 10th International on Applied greenhouse gas emissions from thethe building sector. These systems require high which are Conference returned through the heat This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) th Selection and peer-review under responsibility of the scientific committee of the 10 International Conference on Applied Energy (ICAE2018). Peer-review of theconditions scientific committee of ICAE2018 – The 10th International on Applied Energy. sales. Due under to theresponsibility changed climate and building renovation policies, heat demandConference in the future could decrease, Energy (ICAE2018). prolonging the investment return period. Keywords: Sumart community; Energy demand estimation; Real time; Populartime; Compositional kriging The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Keywords: Sumart community; Energy demand estimation; Real time; Populartime; Compositional kriging forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district 1.renovation Introduction scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were 1. Introduction compared with results from a dynamic heat demand model, previously developed and validated by the authors. Smart concept considerable attention as aof error system The resultscommunity showed that when only attracts weather change is considered, the margin couldtobe increase acceptable energy for some resilience, applications Smart concept attracts considerable attention aansystem to topic increase energy resilience, support/manage smart life, andlower reduce emissions [1,scenarios 2]. Itasisconsidered). important howintroducing to manage energy (the error community in annual demand was thancarbon 20% for all weather However, after renovation support/manage smart and accurate reduce carbon emissions 2]. Itcommunity isand an renovation important topic how to in manage energy demands scale towards and (depending high effective smart design. Fortunately, recent years, scenarios,inthelocal error valuelife, increased up to 59.5% on [1, the weather scenarios combination considered). demands in local scale towards accurate and high effective smart community design. Fortunately, in recent years, data individual buildings increased are increasingly available owning the development IoT (Internet of Things) The on value of slope coefficient on average within the range to of 3.8% up to 8% per of decade, that corresponds to the data on technologies individual buildings arehours increasingly owning toseason the development (Internet Things) sensors [3, 4]. These data allowavailable for monitoring conditions, human movements, market decrease in the number of heating of 22-139h during the heatingbuilding (depending on of the IoT combination ofof weather and sensors technologies [3, 4].activities These allow monitoring building conditions, human movements, market renovation scenarios considered). On thedata other hand,for function intercept increased for useful 7.8-12.7% per decade (depending onuse the transactions and many other in real-time in cities that will offer new insights [5, 6, 7]. Yet, the transactions and other activities in cities thatthese willfunction offer new useful insights [5, 6, 7].considered, Yet, use coupled scenarios). Thescale values suggestedincould used to[2] modify the parameters for the scenarios and of these data for many local monitoring isreal-time stillbelimited and demonstration experiment applied to realthecities ofimprove these data forbegun. localofscale monitoring is still limited [2]itand these demonstration real cities thejust accuracy heat demand estimations. have been Toward smart community design, is increasingly importantexperiment to clarify applied to what to extent these

have been just begun. Toward smart community design, it is increasingly important to clarify to what extent these © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +81-29-850-2567. * E-mail Corresponding Tel.: +81-29-850-2567. address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.813

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IoT sensor technologies related to people activities help in visualizing and support energy resilience [3]. As the first step of creating such energy resilient smart community, this study attempted to estimate quasi-realtime energy demand on the individual building scale. Our target area was Sumida-ward, Tokyo, Japan. Sumida-ward includes: a popular commercial district, Kinshi-cho; a recent re-developed area, around Tokyo SkyTree tower; and many downtown residential districts such as Kyojima. 2. Materials and methods 2.1. Energy demand estimation by hour in monthly average

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For the energy demand estimation by hour, as a benchmark of the monthly average demand changes, we used the monthly basic units per total floor area which is provided by the Japan Institute of Energy in 2008. Fig. 1 shows the basic unit of energy demand in residential and commercial building by hour on April. The two curves show that the basic units reflect a tendency of people activities: commercial energy demand (red line) increases in working time; on the other hand, residential energy demand (gray line) gradually increases in morning time, working time, and evening time. Fig. 2 shows maps of building use (left) and total floor area (right). We viewed a model [the basic unit in the h-th hour] × [Total floor area of the i-th building] by the building use as the monthly average energy demand in each building at the h-th hour. On the commercial energy demand, we make the model in detail by using quasireal-time data as mentioned in next sub-section. On the residential energy demand, we used the model by assuming that the tendency of energy demand is not depending on a day of the week.

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Fig. 2. Building data: Use (left) and Total floor area (right)

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2.2. Energy demand estimation by hour and by day of the week 2.2.1. Google's Populartime data For the energy demand estimation by hour and by day of the week, we used the “Populartime” data [8] in Sumida-ward in Tokyo, which was collected by a web-scraping of Google Maps via its application programming interface on April 2018. The “Populartime” data is a collection of aggregated and anonymized Google Location History (average popularity over the last several weeks) of the smartphone (Google's Android and Apple's iOS) users. The “Populartime” data show relative population density/congestion in individual shops, restaurants, and other customer facilities, by hour and by day of the week. This data will be useful to monitor quasi-real-time people/urban activities and the resulting energy/electricity consumption. The sample size collected in Sumida-ward is N = 945. Fig. 3 shows the collected sample’s locations and popularity rate from 3:00 to 24:00 by three hours on Friday as an example. Popularity rate rapidly increase at working start time (9:00). At lunch time (12:00), the rate becomes totally peak and active. At night time (21:00 and 24:00), high popularity is only main commercial area (e.g., near train stations area). Furthermore, Fig. 4 shows the collected sample’s popularity rate by hour and by day of the week. 安宵宬孳孶

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2.2.2. Compositional kriging approach The popularity rate is limited to only places with points of interests on Google Maps. To interpolate the popularity rate for all commercial buildings, we used compositional kriging that is a geostatistical spatial interpolation approach (see [9, 10] for compositional data analysis and [11, 12] for geostatistics). Since the level of popularity rate is concentrated to around commercial, business area, we assumed spatial autocorrelation of the popularity rate of commercial building. The compositional kriging approach (i) first translates compositional data to relax its constraint that the sum must be the same. Then, (ii) kriging is applied to the transformed variables, and the missing values are interpolated. Finally, (iii) the interpolated missing values are back-transformed to the original scale. In step (i), the isometric log-ratio (ilr) transformation [13], which is well-used in a literature of compositional data analysis, is given by: 𝑧𝑧𝑧𝑧𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡,𝑑𝑑𝑑𝑑 =

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�𝑦𝑦𝑦𝑦𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡,𝑑𝑑𝑑𝑑+1 �

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where zi,t,d are the original and ilr-transformed popularity rate of the i-th building in t-th time, respectively; D the number of dimensions of the original compositional data, D = 2 for this study because the original data of each can be viewed as two dimensional data: congestion ratio and non-congestion ratio; the original popularity rate data of the i-th building in t-th time. In step (ii), we use regression kriging to the ilr transformed popularity rate, zi,t,d. The basic model of regression kriging [11] is as follow: 𝒛𝒛𝒛𝒛 = 𝑿𝑿𝑿𝑿𝑿𝑿𝑿𝑿 + 𝜺𝜺𝜺𝜺, 𝜺𝜺𝜺𝜺~N(𝟎𝟎𝟎𝟎, 𝑪𝑪𝑪𝑪) (2) where z is a vector of the transformed popularity rate, zi,t,d (N × 1); X is a matrix of the explanatory variables (N × K): distance to the nearest train station, average area of the building, and office ratio of the building (K = 3); β is a vector of trend parameters (K × 1); ε is a disturbance (N × 1); and C is a variance-covariance matrix (N × N). We fit the kriging model (2) for every time t. To consider spatial auto-correlation, the elements of C are given by a function of distance. We used the exponential function, which is formulated as follows: 𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 𝜏𝜏𝜏𝜏 2 exp �− �, Cov�𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖 , 𝜀𝜀𝜀𝜀𝑗𝑗𝑗𝑗 � = c�𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 � = � 𝑤𝑤𝑤𝑤 𝜏𝜏𝜏𝜏 2 + 𝜎𝜎𝜎𝜎 2 ,

(𝑖𝑖𝑖𝑖 ≠ 𝑗𝑗𝑗𝑗) (𝑖𝑖𝑖𝑖 = 𝑗𝑗𝑗𝑗)

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where di,j is the Euclidean distance between i-th and j-th buildings. There are three parameters that characterize the function: nugget σ2, partial-sill τ2, and range w. To predict the unknown popularity rates of buildings, we use following model: 𝑧𝑧𝑧𝑧0 = 𝒙𝒙𝒙𝒙′0 𝑿𝑿𝑿𝑿 + 𝜀𝜀𝜀𝜀0 (4) where subscript 0 denotes a building without the “Populartime” data; and superscript ' the transpose operator. ε0 is predicted by minimizing the expected square error: min 𝜀𝜀𝜀𝜀0 = 𝒄𝒄𝒄𝒄′0 𝑪𝑪𝑪𝑪−1 𝜺𝜺𝜺𝜺 (5) where c0 is a covariance vector (N × 1) between observed and predicted buildings. The predictor of z0 that minimizes the expected square error is analytically derived by the following equation: � = (𝑿𝑿𝑿𝑿′ 𝑪𝑪𝑪𝑪−1 𝑿𝑿𝑿𝑿)−1 𝑿𝑿𝑿𝑿′ 𝑪𝑪𝑪𝑪−1 𝒛𝒛𝒛𝒛 � + 𝒄𝒄𝒄𝒄′0 𝑪𝑪𝑪𝑪−1 �𝒛𝒛𝒛𝒛 − 𝑿𝑿𝑿𝑿𝑿𝑿𝑿𝑿 �� 𝑧𝑧𝑧𝑧̂0 = 𝒙𝒙𝒙𝒙′0 𝑿𝑿𝑿𝑿 where 𝑿𝑿𝑿𝑿 (6) Note that we had to leave out the explanation about the parameter estimation due to limitations of space. In step (iii), popularity rates at all commercial buildings are predicted by back-transforming to the original scale. 3. Result Fig. 5 shows estimated energy demand from 3:00 to 24:00 on Friday by three hours as an example of daily changes. As a colour of polygons becomes red from blue, demand from individual buildings increases. Energy demand increased and continued to be high between 6:00 and 21:00. Since we considered people activities by using Google's “Populartime” data, commercial areas which were especially around Tokyo SkyTree, Kinshi-cho Station, and Ryogoku Station were high during working hours. Demand of large condominiums in north parts of the area and around Hikifune Station were high all day long. For smart community design including energy sharing at local scale,

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the results suggests that large grids would be needed to cover high energy demand in these areas. Fig. 6 shows estimated energy demand at 21:00 by day of the week. Although the changes of demand were very few, demand of the commercial areas above were increased on weekend (Friday, Saturday, and Sunday). Since the energy demands depend on people activities especially in commercial area, the results suggest that we would need to consider uncertainty of them for smart community design. To evaluate the uncertainty of the interpolation by the compositional kriging, Fig. 7 shows the prediction variance of commercial buildings on 21:00 on Friday as an example. The prediction variance was high in areas with few observation points of the popularity rate. we need to note the evaluation of the prediction especially in the these areas is with uncertain. 安宵宬孳孶

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宏宲宺 Fig. 5. Estimated energy demand on Friday by three hours

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Fig. 6. Estimated energy demand at 21:00 by day of the week

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Fig. 7. The prediction variance of the compositional kriging at 21:00 on Friday

It is verified that our results of energy demand estimation are intuitively consistent. Such a demand estimation using locations and activities sensors in real-time would increase in near future. As our results would be beneficial to know changes of energy demands depending on people activities by hour and by day of the week. 4. Concluding remarks We showed that “Populartime” data are useful to estimate energy consumptions from individual commercial buildings as quasi-real time people activity data. To consider the characteristics of the “Populartime” data which are compositional time-series and non-exhaustive, we used the ilr transformation, one of the compositional data analysis techniques, and the kriging, one of the spatial statistical interpolation techniques. In the near future, the developments of GPS and IoT sensors will make it possible to grasp people locations and activities in real-time. At that time, the necessity of energy demand-supply matching considering people activities will increase. The developed approach would be useful to estimate and predict energy demand of each building in real-time. Acknowledgements This research was supported by the Join Research Program Numbers 698 and 827 at CSIS, the University of Tokyo and by the Energy Special Accounting of the Ministry of the Environment, Japan. References [1] Zautra A, Hal J, Murray K. Community development and community resilience: An integrative approach. Community Development. 2008;39: 130–47. [2] Yamagata Y, Seya H. Proposal for a local electricity-sharing system: a case study of Yokohama city, Japan. IET Intelligent Transport Systems. 2014;9:38–49. [3] Li M, Li Z, Vasilakos AV. A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE. 2013;101:2538–57. [4] Yamagata Y, Murakami D, Yoshida T. Dynamic urban carbon mapping with spatial big data. Energy Procedia. 2017;142:2461–6. [5] Batty M, Axhausen KW, Giannotti F, Pozdnoukhov A, Bazzani A, Wachowicz M, Portugali Y. Smart cities of the future. The European Physical Journal Special Topics. 2012;214:481–518. [6] Batty M. Big data, smart cities and city planning. Dialogues in Human Geography. 2013;3:274–9. [7] Batty M. Big data and the city. Built Environment. 2016;42:321–37. [8] Riedmaph M. Populartimes. GitHub Repository. 2018. (Copyright (c) 2018 m-wrzr, Released under the MIT license: https://github.com/mwrzr/populartimes/blob/master/LICENSE.md) [9] Aitchison J. The statistical analysis of compositional data. Chapman & Hall; 1983. [10] Pawlowsky-Glahn V, Egozcue JJ, Tolosana-Delgado R. Modeling and analysis of compositional data. John Wiley & Sons; 2015. [11] Cressie NA. Statistics for spatial data. John Wiley & Sons; 1993. [12] Chun Y, Griffith DA. Spatial statistics and geostatistics: theory and applications for geographic information science and technology. SAGE; 2013. [13] Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barcelo-Vidal C. Isometric logratio transformations for compositional data analysis. Mathematical Geology. 2003;35:279–300.