🔋 Energy Prediction Model Methodology

LSTM Neural Network with PSO Optimization

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1

Data Collection

Source: Steel Industry Energy Consumption Dataset (UCI ML Repository)

2.1M
Records
24
Manufacturing Plants
3
Years (2020-2023)
15-min
Intervals
🏭 Facilities: Metal fabrication, food processing, plastics, automotive parts
👥 Employee Range: 50-500 per facility
📊 Data Points: Energy usage, temperature, production volume, operational status
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2

Stratified Random Sampling

Smart data sampling to ensure representative coverage while managing computational requirements

Stratification
By facility type, shifts, seasons
Selection
35% random (seed=42)
Validation
K-S test (p > 0.05)
✓ Maintains statistical distributions (mean, variance, seasonal patterns)
✓ Ensures all operational scenarios are represented
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3

Data Preprocessing

Preparing data for optimal neural network training

🔧 Missing Values: Forward-fill interpolation
📏 Normalization: Min-max scaling for all features
🪟 Sequence Generation: Sliding windows (96 time steps = 24 hours)
📂 Data Split: 70% training (2020-2021), 15% validation (early 2022), 15% testing (late 2022-2023)
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4

Feature Selection (Genetic Algorithm)

Automated identification of most predictive variables

23 → 7
Features Reduced
96%
Predictive Power Retained
⭐ Selected Features: Total power consumption, outdoor temperature, production volume, equipment status, day of week, hour of day, holiday indicators
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🧬

Genetic Algorithm in Action: Feature Evolution

Watch how GA evolves feature combinations through selection, crossover, and mutation

Generation 1 / 20
Population (Feature Chromosomes)
🎯
Selection
Best performers reproduce
🔀
Crossover
Combine parent features
⚡
Mutation
Random feature changes
🏆 Best Feature Set Found
Fitness: 96% Predictive Power
Available Features (23 candidates)
Power consumption Temperature Production volume Equipment status Day of week Hour Holiday Humidity Wind speed Shift type ...
🧬 Evolution: Each generation improves feature combinations through natural selection
📊 Fitness Function: Measures predictive accuracy of each feature combination
✂️ Dimensionality Reduction: From 23 features to optimal 7 features
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5

Hyperparameter Optimization (PSO)

Particle Swarm Optimization for finding optimal model configuration

Particles
20
Iterations
50 (converged at 35)
Parameters
w=0.7, c1=1.5, c2=1.5
7.2% → 4.1%
MAPE Improvement
🎯 Optimized: Learning rate, dropout rates, LSTM units, batch size, sequence length
↓
🔬

PSO in Action: Swarm Optimization

Watch particles explore the hyperparameter space to find optimal configuration

Hyperparameter Search Space
⭐
0
Iteration
7.2%
Best MAPE
20
Active Particles
Particle Movement Formula
velocity = w × velocity + c1 × rand() × (pBest - position) + c2 × rand() × (gBest - position)
w = 0.7
Inertia
c1 = 1.5
Cognitive
c2 = 1.5
Social
🐦 Swarm Behavior: 20 particles explore together, sharing information about good solutions
🎯 Personal Best: Each particle remembers its best-found configuration
🏆 Global Best: Swarm converges toward the best solution found by any particle
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6

LSTM Neural Network Architecture

Three-layer stacked LSTM for sequence learning

Layer 1
128 neurons
Layer 2
64 neurons
Layer 3
32 neurons
📥 Input: 96 time steps (24 hours of 15-min intervals)
📤 Output: 96 time steps (24-hour forecast)
🧠 Capability: Remembers temporal patterns, understands day-of-week variations, seasonal changes
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📊

LSTM in Action: Sample Data Flow

Watch how LSTM processes 24 hours of energy data to predict the next time step

Sample Input: Energy Consumption (kWh)
🧠
LSTM Layer 1
128 neurons
→
🧠
LSTM Layer 2
64 neurons
→
🧠
LSTM Layer 3
32 neurons
Memory Cell State (Hidden Information)
Monday Pattern
Morning Peak
Temperature Effect
Predicted Next Value
--
kWh
🔄 Sequential Processing: Each time step updates the internal memory state
💾 Long-term Memory: Remembers patterns from hours ago (Monday mornings, seasonal trends)
🎯 Contextual Prediction: Uses entire sequence history to predict next value
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7

Model Training

Training through backpropagation with millions of parameter adjustments

🎓 Learning Process: Compares predictions to actual consumption, minimizes error through gradient descent
🔍 Pattern Discovery: Automatically identifies efficiency opportunities (e.g., high-energy processes during cooler hours)
⚡ Optimization: Millions of internal parameters adjusted to capture complex energy patterns
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8

Deployment & Real-Time Applications

AI-powered energy management system with reinforcement learning

⚠️ Anomaly Detection: Flags unusual consumption within minutes, catches equipment malfunctions early
📅 Schedule Optimization: Suggests equipment schedules to minimize costs while meeting production requirements
❄️ HVAC Integration: Coordinates high-energy processes with cooling demands
🤖 Continuous Learning: Reinforcement learning continuously improves recommendations
Active Stage
Inactive Stage