Increasing Renewable Access in the Philippines through AI
Increasing Renewable Access in the Philippines through AI
July 17, 2021
DISCLAIMER: This document is strictly private, confidential and personal to its recipients and should not be copied, distributed or reproduced in whole or in part, nor passed to any third party.
Omdena Philippines Chapter
PH ranks average in sustainable energy adoption�53 points in planning; 60 in incentives & regulatory support
Increasing Renewable Access in the Philippines through AI
July 17, 2021
:
Planning for renewable energy expansion indicator
Rank 1
Rank 51
Rank 131
Rank 1
Rank 96
Rank 130
Incentives and regulatory support indicator
Source: https://rise.esmap.org/analytics
In 2030, PH gov’t solar energy capacity �installation target to increase by 28K %
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Source: https://www.doe.gov.ph/sites/default/files/pdf/nrep/nrep_books_021-087_re_plans_programs.pdf
Sector | Installed Capacity, (MW) as of 2010 | Target Capacity Addition by | Total Capacity Addition (MW) 2011-2030 | Total Installed Capacity by 2030 | |||
2015 | 2020 | 2025 | 2030 | ||||
Geothermal | 1,966.0 | 220.0 | 1,100.0 | 95.0 | 80.0 | 1,495.0 | 3,461.0 |
Hydro | 3,400.0 | 341.3 | 3,161.0 | 1,891.8 | 0.0 | 5,394.1 | 8,724.1 |
Biomass | 39.0 | 276.7 | 0.0 | 0.0 | 0.0 | 276.7 | 315.7 |
Wind | 33.0 | 1,048 | 855.0 | 442.0 | 0.0 | 2,345.0 | 2,378.0 |
Solar | 1.0 | 269.0 | 5.0 | 5.0 | 5.0 | 284.0 | 285.0 |
Ocean | 0.0 | 0.0 | 35.5 | 35.0 | 0.0 | 70.5 | 70.5 |
Total | 5,438.0 | 2,155.0 | 5,156.5 | 2,468.8 | 85.0 | 9,865.3 | 15,304.3 |
�������
Project Goals
Learning Outcomes
Increasing Renewable Access
in the Philippines through AI
Project Pipeline
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Input
Data Preparation
Modelling
Output
Energy Demand
Create light vs no light mask computed using Philippines 2020 dataset
Estimate solar energy availability and consider where are the optimal geo-locations
Partition PH domain as Laguerre-Voronoi regions (DHS data)
Clean, aggregate installed electric capacity (power plant data)
Convert vector data to raster; re-classify
Multi-criteria weighted overlay analysis
K-Means
Clustering
Energy Availability
Night-time�Light �Density
Annual VNL 2 from Earth Observation Group
2017 PH National Demographic and Health Survey (DHS) Access
to Electricity��2020 PH List of Existing Power Plants��FB High Resolution Settlement Layer (2018 PH population data)
Satellite image of Solar Irradiance(GHI, DHI, DNI) and Environmental Temperature;��Satellite Image of WorldBank
Task 2 - Night-time Light Density
���
Increasing Renewable Access in the Philippines through AI
July 17, 2021
The nightlight data was obtained from Earth Observation Group. The dataset is called “Annual VNL 2”, a postprocessed version of VIIRS nighttime lights.
The satellite imagery captures the light emitted at night. It measures the radiance as nW/cm2/sr (nanowatt per square centimeter per steradian).
Annual VNL 2 has the advantage of being preprocessed for noise filtering, zeroing of the background with no detectable lightning, and outlier removal.
For this project, we used Philippine data for the year 2020.
Task 2 - Energy Demand
Increasing Renewable Access in the Philippines through AI
July 17, 2021
We determined settlements from FB’s high resolution settlement layer (HRSL) of high-energy demand using multi-criteria weighted overlay analysis.
We first converted the following source vector data to appropriate rasters:
We then re-classified the response of each input raster (night-time light, DHS, installed electric capacity) and obtained a total score based on site suitability (low night light, access to electricity, and installed electric capacity).
Task 2 - Energy Demand
Increasing Renewable Access in the Philippines through AI
July 17, 2021
We determined settlements from FB’s high resolution settlement layer (HRSL) of high-energy demand using multi-criteria weighted overlay analysis.
We first converted the following source vector data to appropriate rasters:
We then re-classified the response of each input raster (night-time light, DHS, installed electric capacity) and obtained a total score based on site suitability (low night light, access to electricity, and installed electric capacity).
Task 4 - Estimating Energy Availability
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Using single diode model circuit we are, using norton’s theorem we are finding the total resistance of the circuit since we know the Vmax and Imax we wil get the total resistance.
Task 4 - Estimating Energy Availability
Increasing Renewable Access in the Philippines through AI
July 17, 2021
TOP 10 LOWEST PVOUT(JAP6-72-320/4BB) MUNICIPALITY IN THE PHILIPPINES
TOP 10 HIGHEST PVOUT(JAP6-72-320/4BB) MUNICIPALITY IN THE PHILIPPINES
Task 4 - Estimating Energy Availability
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Single Diode Formula
Single Diode Formula:
Most of the barangay belong to the clusters with moderate and high estimated energy availability. This demonstrates how viable solar energy is as renewable energy source in the Philippines.
World Data Bank:
Values from the global atlas show that most of the barangays can be classed as having low and moderate estimated energy availability��Recommendations:
For future implementation of the similar concept or a continuation of this task, deep clustering algorithms along with tuning of the hyper parameters can be added to the methodology. Along with the validation of the computed estimated energy available for areas where similar data has already been collected. �
World Data Bank
Cluster | Value (kWh) | No. of Barangays | Percentage |
Low | 117 - 1134.83 | 4333 | 10.33% |
Moderate | 1134.83 -�1313.50 | 16373 | 39.05% |
High | 1313.50 – 1487.80 | 14236 | 33.95% |
Very High | 1487.80 – 1752.10 | 6991 | 16.67% |
Model | Silhouette Scores | Number of Clusters |
K Means | 0.54013 | 4 |
Gaussian Mixture | 0.5396 | 4 |
DBScan | 0.2747 | 19 |
Model | Silhouette Scores | Number of Clusters |
K Means | 0.5444 | 5 |
Gaussian Mixture | 0.5419 | 5 |
DBScan | 0.1494 | 20 |
Cluster | Value | No. of Barangays | Percentage |
Very Low | 117 – 1312.96 | 2577 | 6.15% |
Low | 1312.96 – 1395.98 | 12234 | 29.18% |
Moderate | 1395.98 – 1464.33 | 13012 | 31.03% |
High | 1464.33 – 1552.86 | 9691 | 23.11% |
Very High | 1552.86 – 1695.31 | 4419 | 10.53% |
Task 4 - Estimating Energy Availability
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Single Diode Formula
Municipality PVOut Clustering:
And based on the Clustering, it is also the same as the which is composed of 5 cluster, based on the value of PVOut of each municipality of the philippines.
Recommendation:
For the Lowest PVOut Area, we are suggesting if they can explore any type of renewable energy especially Wind Energy because mostly on the lowest PVOut area the weather there are cloudy, and low temperature which is possible have a high wind strength.
World Data Bank
Task 1&6 - Visualization & Integration
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Omdena Philippines Chapter
Task 1&6 - Visualization & Integration
Increasing Renewable Access in the Philippines through AI
July 17, 2021
https://omdenaph-solar.vercel.app/ ��Built with React, Next.js, Vercel, and Mapbox.
Task 1&6 - Visualization & Integration
Increasing Renewable Access in the Philippines through AI
July 17, 2021
We started with plotting the different power plants and energy sources in the Philippines. This way we identified various municipalities that are still off-grid based on the 2020 Department of Energy data. These are marked with Diamonds
Task 1&6 - Visualization & Integration
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Our Web App has 4 map modes in order to highlight different information in our App. Below the Map Mode Menu, you can see the municipalities ranked by their need for solar energy. They are also highlighted in the map by Dots (see 'Marker Labels' legend).
Task 1&6 - Visualization & Integration
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Our suggested places and settlements (estimated by Task 2 & 4) are marked in Green Dots for Area and Blue Dots for more granular Settlements data. When you click on the Green Dot for Area, you'll be shown more information about the area in the sidebar.
Task 1&6 - Visualization & Integration
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Likewise, there is also a list of Areas/Municipalities below the Map Mode option. When you click on the Area, like 'Tapaz, Capiz', you'll be directed to its location, with the municipality's information at the sidebar.
Task 1&6 - Visualization & Integration
Increasing Renewable Access in the Philippines through AI
July 17, 2021
We matched the demand data with an estimated output that solar energy will produce for that area. We start with the population and settlements data from 2017 DHS data and settlements estimate from the our geospatial analysis.
Task 1&6 - Visualization & Integration
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Using the Solar Global Atlas and World Bank datasets, we estimate the mean PVOut that installing solar energy could produce for that Area/Municipality. The granular settlements (in Blue Dots) are also marked for installing solar micro grids all over the municipalities.
Recommendations
Determine clusters of settlements that can be serviced by ground mount solar panels
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Active Collaborators
Increasing Renewable Access in the Philippines through AI
July 17, 2021
Bryce Mercines |
Michael Brian Manuel |
Arjay Ronald Hosmillo |
Andrea Marie Maderazo |
Albert Yumol |
Jose Marie Antonio Minoza |
Neil Ruaro |
Dana Redeña |
Cyndi Ignacio |
Raphael Bihis |
Joshua Cortez |
Carl Terence Valdellon |
Bryan Pajarito |
Abegail Lagar |
Jofer Santiago |