SolarCastAI

The Future of Solar Energy Forecasting

Revolutionizing intraday solar energy trading through AI-powered computer vision and real-time forecasting. Our cutting-edge technology combines satellite imagery, weather data, and machine learning to predict solar energy output with unprecedented accuracy.

AI-Powered

Advanced machine learning algorithms analyze complex weather patterns

Computer Vision

Real-time satellite and ground-based image analysis

Trading Optimization

Maximize profits through precise energy market predictions

20-30% Higher AccuracyReal-time PredictionsOptimized Trading

The Challenge

Solar energy operators face critical challenges that cost millions in lost revenue

Unpredictable Output

Weather changes cause solar production to fluctuate wildly, making accurate forecasting nearly impossible with traditional methods.

Forecast Accuracy60-70%
High Imbalance Costs

Inaccurate forecasts result in costly penalties and imbalance charges that can reach thousands of euros daily.

€50,000+
Monthly imbalance costs
Limited Grid Integration

Poor forecasting limits renewable energy integration, slowing the transition to clean energy.

Grid StabilityAt Risk

Our Revolutionary Solution

SolarCastAI combines cutting-edge AI with local computer vision for unprecedented accuracy

360° Computer Vision

Local cameras capture real-time sky conditions, cloud movement, and solar angles with precision that satellites can't match.

Adaptive AI Models

Machine learning algorithms continuously adapt to local weather patterns and plant characteristics for maximum accuracy.

Real-time Integration

Seamless integration with SCADA systems and trading platforms for instant forecast-to-bid decisions.

90%+
Forecast Accuracy
20-30%
Accuracy Improvement
15min
Forecast Resolution

Technology Stack

Cutting-edge AI and computer vision technologies working in harmony

Computer Vision Engine
Cloud detection & tracking
Deep learning models analyze satellite imagery to identify cloud formations with 95% accuracy
Solar angle calculation
Precise astronomical calculations for optimal panel positioning and irradiance prediction
Irradiance modeling
Physics-based models predict solar radiation with sub-hourly granularity
Real-time processing
Edge computing processes satellite data with <50ms latency for instant updates
AI/ML Pipeline
Time-series forecasting
LSTM and Transformer networks achieve 92% accuracy for 24-hour solar predictions
Adaptive learning
Continuous model retraining adapts to local weather patterns and equipment changes
Multi-modal data fusion
Combines satellite imagery, weather data, IoT sensors using ensemble learning
Continuous optimization
AutoML pipelines automatically tune models based on performance metrics
Integration Layer
SCADA connectivity
Secure industrial protocols connect to plant control systems with 99.9% uptime
Weather API integration
Real-time feeds from NOAA, ECMWF aggregating 50+ meteorological parameters
Trading platform APIs
Direct integration with energy markets enabling automated bidding strategies
Real-time dashboards
Interactive interfaces with customizable KPIs and predictive analytics

Product Overview

Experience the future of solar forecasting through our interactive platform showcase

Real-time Dashboard

Live Performance Monitoring

API Integration

Seamless System Connection

Trading Signals

Automated Market Intelligence

Real-time Dashboard

Live Performance Monitoring

Monitor your solar plant performance, weather conditions, and forecasts in real-time with our intuitive dashboard.

Live DataMobile ResponsiveCustomizable

Performance Metrics

Real-time system performance

Data Points10M+
Update Frequency1 min
Visualization Types15+
Live data updating

Market Opportunity

Massive growth in solar energy and AI markets creates unprecedented opportunity

Solar Growth

+25%

Annual capacity growth in Europe

Trading Volume

+40%

Intraday market growth annually

AI Market

€2.4B

AI in energy by 2027

Expansion Strategy
1

Montenegro & Serbia

Initial market validation

2

Regional Expansion

Slovenia, Croatia, North Macedonia

3

European SaaS

Full European market penetration

Revenue Projections
Year 1€150K
Year 2€450K
Year 3€900K

Competitive Landscape

SolarCastAI's unique positioning in the solar forecasting market

Our Competitive Edge

Advanced AI Integration

Proprietary computer vision algorithms

20-30% More Accurate

Intraday Specialization

Purpose-built for short-term trading optimization

Trading Focused

Cost-Effective Solution

Software-only approach with faster deployment

Lower TCO

Market Competition

Traditional Weather Services

AccuWeather, Weather Underground

General weather forecasting with limited solar-specific capabilities

Standard accuracy

Energy Management Systems

Schneider Electric, ABB

Hardware-focused solutions with basic forecasting features

Hardware dependent

SolarCastAI Advantage

First-mover advantage in AI-powered intraday solar forecasting

20-30% Higher Accuracy

Expert Team

Proven expertise in energy systems, AI, and trading

Marko Šćepanović

System Architect

10+ years implementing MDMS for major TSO/DSO operators across Western Balkans

Energy SystemsData ArchitectureSCADA
Ognjen Miletić

ML Engineer - Time Series

Specialist in predictive modeling and intraday electricity forecasting

Time SeriesForecastingML Pipelines
Milija Bajčeta

ML Engineer - Computer Vision

7 years ML experience in environmental imagery and spatiotemporal modeling

Computer VisionObject TrackingSatellite Imagery
Miloš Tasovac

Frontend Developer

Lead developer of Nimbus Weather Data Studio with expertise in real-time visualization

Data VisualizationReal-time UIDashboards
Zoran Đukanović

Energy Trading Expert

Solar plant owner and trader with expertise in intraday markets across SE Europe

Energy TradingSolar OperationsMarket Analysis
Vesna Vuksanoviċ

Backend Developer

Experienced backend developer specializing in scalable systems and data processing

JavaPythonBackend Systems

Financial Overview

Strategic investment for maximum impact and growth

Total Project Budget
€223,523

18-month development cycle

Own funding (20%)
€44,704.6
Requested grant (80%)
€178,818.4
Budget Allocation
Staff (49%)€108,600
Equipment€54,500
External Service€29,800
Training Cost€16,000
Indirect Cost€14,623

Risk Assessment

Comprehensive risk analysis with proven mitigation strategies

Technical Risks

Model Accuracy Degradation

AI models may lose accuracy over time due to changing weather patterns

Continuous learning systems

Data Quality Dependencies

Reliance on third-party satellite and weather data providers

Multiple data source partnerships

Scalability Challenges

Processing large volumes of real-time data across regions

Cloud-native architecture
Market Risks

Regulatory Changes

Energy market regulations could impact trading strategies

Flexible platform design

Competitive Response

Large tech companies entering solar forecasting space

Strong IP portfolio & first-mover advantage

Customer Acquisition

Energy companies may be slow to adopt new technologies

Pilot programs & proven ROI
Risk Mitigation Success
95%

System Uptime Target

3+

Backup Data Sources

24/7

Monitoring & Support

Development Roadmap

TRL 5-8 experimental development approach to market-ready solution

Phase 1
Technology Validation

Months 1-6

  • Data pipeline setup
  • Basic AI models
  • System architecture
Phase 2
System Demonstration

Months 7-12

  • Install at 1-2 plants
  • Real-time testing
  • Model refinement
Phase 3
Prototype Validation

Months 13-15

  • Accuracy validation
  • ≤10% MAPE target
  • Performance optimization
Phase 4
System Complete

Months 16-18

  • Market simulation
  • SaaS platform launch
  • Regional scaling
Key Performance Indicators

Forecasting Accuracy

Target MAPE≤10%

15-min resolution forecasts

System Deployment

Pilot Sites2 plants

Operational solar installations

System Reliability

Uptime Target≥95%

24/7 operational availability

Investment Opportunity

Join us in revolutionizing solar energy forecasting and accelerating the renewable energy transition

Why SolarCastAI?

Proven ROI

20-30% accuracy improvement = significant cost savings

Expert Team

Proven track record in energy systems and AI

Unique Technology

First-of-its-kind local computer vision approach

Scalable Market

Expandable across European renewable energy markets

Investment Request
€178,818

To validate technology and scale regionally

400%+
Expected ROI in 3 years
6 months
Time to market entry
€1.5M
Revenue target in 3 years
40+
Target clients in 3 years

Position Montenegro as a Leader in AI-Driven Renewable Energy

This investment will establish Montenegro as a pioneer in renewable energy AI technology, creating jobs, attracting international partnerships, and contributing to the global clean energy transition.