Comprehensive validation of a simulation system for simultaneous prediction of urban climate and building energy demand

Transcription

1 ICUC9, Friday, 24/Jul/2015, Toulouse, France Comprehensive validation of a simulation system for simultaneous prediction of urban climate and building energy demand Yukihiro Kikegawa (Meisei University, Tokyo, Japan) Osaka City (population 2.7M) Yukitaka Ohashi (Okayama Univ. of Science), Tomohiko Ihara (the University of Tokyo), Minako Nabeshima (Osaka City University), Yoshinori Shigeta (Tottori University of Environmental Studies)

2 Background & Purpose Progress in Urban Climate Modelling Heterogeneous LULC & surface geometry, and those parameterizations Slab models (1990s) UCPs (Urban Canopy Parameterizations) with CFD (2000s-) Anthropogenic Heat (AH), and its implementation to urban climate models Static approach (1990s) Energy inventory-based AH fed into meso models Dynamic modelling (UCP-BEM) (2000s-) Integration of Building Energy Model (BEM) to UCP to represent interaction between climate and energy. 多層都市キャノピー Slab model Urban Canopy Model The first UCP-BEM (Kikegawa et al., 2003) was implemented in the WRF model, and a new system WRF-CM-BEM was developed (Kikegawa CFD et al., 2014). Urban More AC Warming Goal of Study demand Positive To substantiate the performance of WRF-CM-BEM in Feedback coupling simulation of urban climate and building electricity demand, and to check its potential More as CO an urban energy management tool especially in the prediction of photovoltaic 2 power generation toward the contribution to smart Additional grid technology. AH Y. Ashie, Urban Climate News, No.41, 2011

3 Models (WRF-CM-BEM, Kikegawa et al., TAAC, 2014) WRF-ARW CM (Kondo & Kikegawa, PAGEOPH, 2003, Kondo et al., BLMet, 2005) BEM (Kikegawa et al., Appl. Energy, 2003 & 2006) CM-BEM: the first model for coupled simulation of urban canopy climate and building energy demand WRF Upper Atmosphere Fluxes as bottom boundary conditions WRF 1st layer Scalars as top boundary conditions Constant Flux Layer Roughness Sublayer z * Noah-LSM (nonurban grids) CM-BEM (urban grids) Canopy layer Two-way coupling of CM-BEM with WRF

4 Yearlong field campaign in Osaka (FY 2013) 1/2 In 15 urban areas, at a couple of rooftop and ground sites in each area. Mean distance between rooftop(+) sites = 3.8km (m) Japan Sea Tokyo Osaka Pacific Ocean Osaka City Osaka Bay Downtown Commercial areas: C1~C3 Residential areas : R1~R9 Mixed-use areas : M1~M3 Rural: ground site O1 Commercial Residential

5 Yearlong field campaign in Osaka (FY 2013) 2/2 Period: March 2013 March 2014 Meteorological Measurements - Rooftop level (3 to 5- storey school buildings) temp., humid., atmos. pressure (every 10 min.) S global irradiance, L (every 5 min.) - Ground level ( at 2.5m, open space near rooftop sites) temp., humid. (every 10 min.) Electricity demand monitoring Areal & hourly electricity demand monitored at 13 distribution substation each located in 13 observation areas with horizontal dimensions of 500 m to 2 km square each (except M2 & R3 areas). Meteorological elements Electricity demand data CM validation (ex. temp. in UCL) BEM validation S global irradiance WRF validation (S & its spatial inhomogeneity) Potential in the prediction of PV power generation in urban area?

6 MAPD (%) of 5-min-mean S (each site vs. reference site) Results 1/4 (Observed intraurban inhomogeneity in S ) Period: 14 March March 2014 ( LST) 16 observation sites for S Unbiased Root Mean Square Deviation = ±1σ of (S at a site - S at C1) MAPD: Mean Absolute Percentage Deviation (S at a site - S at C1) Osaka City Statistical characteristics of S inhomogeneity in Osaka 50 were quantified based on the measurements to be used for the model validation Observed MB 2.5Intraurban spatial inhomogeneity in S 0 becomes larger on lightly & partly C1 C2 C3 M1 M2 R1 R2 R3 R4 R5 R6 R7 R8 M3 O1 cloudy days than that on sunny & overcast days maybe due to partly cloud cover 2in the sky, 5 showing 7reasonable 10 dependency Observed on URMSD distances (±1σ) between each site and reference Distance site from (larger C1 (km) unbiased RMSD & MAPD at more distant sites).

7 Mean Bias and Unbiased RMSD of 5-min-mean S (W/m 2 ) Preliminary Results 2/4 (Simulated intraurban inhomogeneity in S ) MAPD (%) of 5-min-mean S (each site vs. reference site) Period: Jul Aug ( LST) Summer Simulation: Δx,Δy = 1km, Cloud Microphysics = Thompson et al. scheme Each site vs. reference site C1 400 Cloudless Lightly cloudy Partly cloudy 300 (SD 9hrs) (9>SD 7hrs) (7>SD 2hrs) SD: sunshine duration Overcast (2hrs>SD) However, overall statistical Groups & features Sites of S inhomogeneity Some disagreements between 10 observations and simulations ( Over/under-estimation of MB and URMSD) 0 C1 suggesting C2 C3 M1 M2 R1 R2 R3 R4 a R5 potential R6 R7 R8 M3 O1 of its application to detailed Distance from C1 (km) Observed MB Observed URMSD Simulated MB Simulated URMSD seem to be roughly reproducible by WRF-CM-BEM so far, evaluation of photovoltaic power generation in urban areas.

8 Air Temperature (C ) 7/30 0h Preliminary Results 3/4 (Simulated air temperatures) 7/31 0h 8/1 0h 8/2 0h 8/3 0h 8/4 0h 8/5 0h 8/6 0h 8/7 0h 8/8 0h 8/9 0h 8/10 0h 8/11 0h Period: 0000 LST 30 July LST 12 August 2013 (13 days) RMSE :Root-Mean-Square Error MAPE: Mean Absolute Percentage Error urban ground-sites averages Obs.(2.5m) WRF-CM-BEM(2m) Sample : 4159 RMSE = 1.39 C MAPE = 3.58 % cf. Salamanca et al. [JGR,2014] Sample : 720 RMSE = 1.7 C MAPE = 3.9 % New UCP-BEM named BEP + WRF-CM-BEM shows good performance in terms of BEM, similar to our CM-BEM, was applied to Phoenix USA with WRF for the simulation of 10 day extreme heat period, and the results (2m temp.) were compared to the observations at 3 urban weather stations. reproducibility Date of & the Time near-surface (LST) urban climatology over Osaka Temporal in variations summer of observed so far, compared and simulated with the result in recent surface Daily course air temp. of near-surface air temp. was satisfactorily simulated! investigation which used UCP-BEM model.

9 7/30 0h 7/31 0h 8/1 0h 8/2 0h 8/5 0h 8/6 0h 8/7 0h 8/8 0h 8/9 0h Areal & hourly electricity demand ( W/floor-m 2 ) Preliminary Results 4/4 (Simulated electricity demand) Period: 30 July - 12 August 2013 (results on 9 weekdays in C2 & R7) Areal & hourly electricity demand ( W/floor-m 2 ) 0 60 Obs. (C2) (C2) WRF-CM-BEM WRF-CM-BEM (C2) (C2) 50 Obs. (R7) (R7) WRF-CM-BEM WRF-CM-BEM (R7) (R7) C2 area: MB = W/floor-m2, MAPE = 21.7% 7/30 0h 7/31 0h 8/1 0h 8/2 0h 8/5 0h 8/6 0h R7 area: MB = W/floor-m2, MAPE = 52.9% 8/7 0h 8/8 0h Date Date & Time & Time (LST) (LST) Osaka City Osaka Bay Temporal Possible causes variations of observed and simulated hourly areal electricity 1. Changes demand in human in C2 behavior & R7 areas after 2011 on the disaster 9 weekdays. (about 20% energy-saving, (Electricity but not yet demand considered is normalized in the simulation) by building floor area) 2. Less realistic settings of BEM parameters? (building materials, HVAC, etc.) 8/9 0h Overestimations!

10 Conclusions & Ongoing works The original system for the simulation of the interaction between building energy demand and urban climate, named WRF-CM-BEM, is used. To substantiate the system performance, yearlong field campaign was conducted in Osaka city. Multi-site measurements on UCL climatology and areal electricity demand have been obtained for the model validation. Statistical characteristics of Intraurban spatial inhomogeneity in observed S are being quantified with significant site-by-site fluctuations. WRF-CM-BEM seems to be able to roughly reproduce those observed S inhomogeneities suggesting its potential application to evaluation of PV power generation. Preliminary analyses suggest promising performance of WRF-CM-BEM so far, in terms of reproducibility of the near-surface air temperature and areal building electricity demand but with a certain overestimation on the latter. Further validations are being carried out using yearlong measurements to clarify the ability of WRF-CM-BEM in coupling simulation of urban climate and building electricity demand.