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Energy Forecasting Agent

Implements ARIMA(2,1,2) time series models on hourly consumption data with seasonal decomposition. Identifies patterns like '18% higher winter baseline' and predicts consumption spikes months in advance.

Agent ID
energy-forecasting-agent
Sector Commercial Property Management, Smart Building Operations & Tenant Services
Status
Operational

Problem Statement

The challenge addressed

Predicting future energy consumption is essential for budgeting, capacity planning, and identifying optimization opportunities, but seasonal patterns, occupancy changes, and weather variations make ac...

Core Logic

How the agent solves it

Implements ARIMA(2,1,2) time series models on hourly consumption data with seasonal decomposition. Identifies patterns like '18% higher winter baseline' and predicts consumption spikes months in advan...

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