AI-Driven Energy Solutions for Smart Cities

The Future of Urban Energy Optimization

In the next decade, urban energy demands will surge by 45%. Our AI-powered energy optimization platform addresses this challenge by combining predictive analytics, real-time grid monitoring, and reinforcement learning to achieve city-level energy surplus reductions of up to 32% annually.

2025

Real-time energy usage optimization

How It Works

Live demo

Our system uses deep reinforcement learning to continuously adapt to shifting demand patterns, weather conditions, and energy prices. This AI core coordinates distributed energy resources:

Smart grid load balancing
Dynamic pricing signals
Demand response optimization
Microgrid coordination

~32%

Energy surplus reduction

Technical Architecture

Neural Network Design

class EnergyOptimizer {
    constructor() {
        this.model = tf.sequential()
        this.model.add(tf.layers.conv1d({
            inputShape=[24, 17],
            filters: 32,
            kernelSize: 3,
            activation: 'relu',
            kernelConstraint: tf.constraints.maxNorm({ minNorm: 0.001, maxNorm: 2 })
        }))
        this.model.add(tf.layers.flatten())
        this.model.add(tf.layers.dense({
            units: 128,
            activation: 'relu'
        }))
        
        this.optimizer = tf.train.adam({learningRate: 0.001});
    }

    train(data) {
        return this.model.fit(data, {
            epochs: 200,
            validationSplit: 0.2,
            callbacks: {
                onEpochEnd: (epoch, logs) => this.evaluateMetric(logs)
            }
        });
    }

    evaluateMetric(logs) {
        // Custom evaluation metric
        this.evalHistory.push({
            loss: logs.loss,
            valLoss: logs.valLoss
        });
    }
}

// Usage patterns
const optimizer = new EnergyOptimizer();
const metrics = optimizer.train(trainingData);