B

KEPCO Power Data Analytics API

3.15

Derivation Chain

Step 1 KEPCO earnings and rising AI power demand
→
Step 2 Demand for power data-based services
→
Step 3 Power usage data cleansing and analytics API

Problem

With AI infrastructure and data center power demand surging, energy management startups and ESG reporting companies need electricity usage data. However, KEPCO (Korea Electric Power Corporation) data is non-standardized and difficult to classify by building type or time period, requiring 2–4 weeks of developer effort to process. Teams end up rewriting parsing and normalization code for every project.

Solution

A service that cleanses KEPCO power data (including public datasets) and provides (1) a standard REST API for electricity usage by building type, region, and time period, (2) time-series analysis endpoints (peak detection, baseload, pattern recognition), and (3) carbon emission conversion data for ESG reporting. Differentiated developer experience via OpenAPI 3.0 docs and Python/JS SDKs.

Target: Backend developers at energy management and ESG reporting startups (3–20 employees), ages 25–35
Revenue Model: Usage-based API pricing at KRW 1,900 per 1,000 calls (~$1.40), monthly minimum of KRW 29,000 (~$22). Free tier: 500 calls/month.
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (68%)

Tech Complexity
29.3/40
Data Availability
18.8/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (58/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
12.0/15
Solo Buildability
5.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Data Pipeline [medium] Backend [medium] Infrastructure [low]
Dashboard