Summary
Entrusted Study and Examination Work Concerning the Introduction Situation ofPV Power Generation Systems, etc. through AI Analysis, etc. in FY 2021
The Ministry of the Environment (MoE) has been continually implementing the “Studyon the Introduction Potential of Renewable Energies” and the “Development of Basic
Zoning Information Concerning Renewable Energies” since FY 2009 as part of its efforts
to consolidate an information base on the potential of all kinds of renewable energies,
such as PV power, wind power, small and medium-scale hydropower, geothermal heat,solar heat and underground heat. While converting such information on the potential ofrenewable energies into cartographic information, the MoE disclosed the Renewable
Energy Potential System (REPOS) superimposing various types of relevant data for easy
understanding in June, 2020.
Following the declaration of carbon neutral in October, 2020, the Government of Japandeclared on 22nd April, 2021 the ambitious target of reducing the emission of GHG by
46% by FY 2030 from the FY 2013 level in line with the FY 2050 target while continuing
its challenge of achieving 50% or more.
To achieve this FY 2030 target, it is essential to maximise the introduction of PV power
generation systems (PV systems) of which the lead time for introduction is relatively short.
One effective way of accelerating PV systems becoming the main force of renewableenergy is for public organizations themselves to make the maximum efforts to introduce
such systems. For this reason, the Regional Decarbonisation Roadmap compiled by theCouncil for National and Local Decarbonisation lists targets for the introduction of PV
systems for central and local government buildings and land.
In the light of these targets, this Entrusted Work aims at continually studying the actualand potential introduction situation of PV systems at public facilities by means of (i)establishing the actual introduction performance of PV systems at public facilities and (ii)conducting a comparative analysis by overlaying the information on actual introduction
to information on the renewable energy potential.
1 ． Preparation of Aerial Images, etc. Required to Establish the Introduction
Situation of PV Systems
Having examined the specification of aerial images and satellite images suitable for AI
analysis, the advantages and disadvantages of candidate aerial images and satellite images
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for purchase were sorted and a comparative evaluation of these images was conducted.
Those aerial images and satellite images which were given a high evaluation score werepurchased and such data was arranged for AI analysis.
To establish the introduction situation and introduction potential of PV systems, theresults of aerial photogrammetry, including the latest high resolution aerial images
possessed by local public bodies, constitute important basic data. Because of this, theactual situation concerning the surveying results possessed by local public bodies wasexamined along with whether or not there is a way to gather the aerial images possessedby local public bodies in a sustainable as well as efficient manner.
2．Survey on Nationwide Introduction of PV Systems Installed at Buildings, etc. (AIAnalysis)
AI analysis was conducted using aerial images and satellite images which had beenarranged for AI analysis as described in 1. above (Fig.-1). The recall ratio was thenchecked to verify the effectiveness of the AI analysis results. A new analysis model wasformed based on the verification results to make the AI relearn to improve the recall ratio(Fig.-2).

Fig.-1 Visualization of PV Detection Results (Aichi Prefecture)

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Sample Area 3
Number of Houses with PV Panel: 108, Number ofHouses Identified: 106 Recall Ratio: 98.15%
1.85 points improvement

Sample Area 2
Number of Houses with PV Panel: 100, Number ofHouses Identified: 99 Recall Ratio: 99.00%
1.00 point improvement

Fig.-2 AI Analysis Results and Verification Results of Improved Recall Ratio

3. Establishment of Nationwide Location Information on Public Facilities
Following examination of the base data specifications pertaining to the acquisition oflocation information on public facilities and comparative evaluation of the candidate basedata, appropriate base data was procured. This base data was then tied to the building
polygon and annotation points for buildings, etc. to establish suitable data.
Table-1 Building Polygon Extraction Results for Public Facilities/Buildings
Types of Annotation Points Such for Buildings, etc.

Type of Building byBuilding Polygon Data

Primary Category

Secondary Category

Type

Transport-related Facility

Road-related

Michi-no-Eki

Government Building――

Government BuildingSchool―SchoolHospital―Hospital(Excluding those listedEntertainment/below)

Commercial Facility

MarketMarketPetrol Station

Petrol Station

Public BathOther Building

Rent-a-car

Other Building(Excluding those listed

Other Building

School/ HospitalEntertainment/
Commercial Facility

Factory/ Bank/ Building

―

―

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Entertainment/
Commercial Facilitybelow)

Accommodation
Accommodation Facility

Facility
Multiple-dwelling
Complex

Factory

Factory

Warehouse

Warehouse―

Accommodation Facility―

Multiple-dwelling
Complex

4. Study on the Installed Capacity and Installation Potential of PV Systems at Public
Facilities
The results of the exercises in 2. And 3. above were overlaid on GIS to extract public
facilities with a PV system and those without a PV system. A PV system installation
coefficient by category of public facility was established based on the panel area of eachpublic facility which was interpreted using aerial images and AI analysis conducted forsome geographical areas. Using this coefficient, the installed capacity at public facilities
with a PV system was established and the installation potential of a PV system at public
facilities without such a system was estimated.

Distribution ofPV system(Point)Distribution of public
facilities (Polygon)

Overlay
Judgement on the PV system on
public building
PV の有無を判別

Fig.-3 Discrimination Image on the Situation of PV System Introduction
by Overlaying Distribution of PV systems and Public Facilities

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Public facility
without a PV
systemPublic facility
with a PVsystem

Table-2

Estimation Results of Installed Capacity of Existing
PV Systems by Attribute Data (Excerpt)

Category of Public Facility

Primary Category
Transport-related
Facility

Secondary Category
Road-related

Type
Michi-no-Eki

Prefectural
Government
OfficeCity Office
Prefectural,
Ward Office
Municipal or Village Town/Village
Government Office Office
City/Town/Village
Branch Office
Offices of 23
Tokyo WardsPublic Office
Government Building
Public Office, etc.

Courthouse
PrisonHealth Centre
Police Station
Fire Station
Tax Office
Post Office
Forest Station
Police Box
CommunityCentreImperial Facility

NumberFloor Area
(m2)

Installed
Capacity of
Existing PV
System(kW)134

157,656

1,574.1

45

225,933

1,360.4

374
77
245

964,672
209,764
408,032

5,808.6
1,263.0
2,456.9477

718,589

4,326.8

725,537

153.8

674
52
103
72
175
424
30
535
4
276
1,861

1,122,758
96,633
540,336
126,887
260,844410,328
30,948769,247
1,277
393,863
1,111,994

14,201.6
1,222.3
6,834.6
1,605.0
3,299.4
5,190.2
391.5
9,730.1
16.2
4,981.9
10,503.0

2
8,308
78.5
Other Public 5,123 8,470,698 80,007.6
Facility(Government Buildings: Sub-Total) 10,556 15,896,649 153,431.2

Other Public Facility

5. Examination for Advancement of the Establishment of the Installation Potentialof PV Systems
A study was conducted on the precedent of a solar light mapping system which
contributes to the accelerated installation of PV systems. This was followed byexamination of (i) the requirements (functional requirements) for a solar light mappingsystem, assuming the need for PV power generation in the coming years, and (ii) the data,etc. required for the actual adoption of such functions.

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