Problem
Husk faced difficulties in accurately forecasting energy consumption across its grids in India and South Africa. These challenges hindered the ability to scale operations effectively, optimize grid pricing, and negotiate favorable energy contracts. Inaccurate energy consumption predictions resulted in inefficiencies in grid operations and pricing strategies. This limited Husk’s ability to meet energy demands cost-effectively. Suboptimal forecasting led to revenue losses, inefficient energy distribution, and missed opportunities for scaling grid operations.
Solution
Husk partnered with Zams to develop multiple time series forecasting models. Zams connected to their live energy usage database leveraged historical energy usage data to provide actionable insights into consumption patterns. The models incorporated key data points such as grid-specific consumption trends, weather patterns, and regional energy demand fluctuations. Advanced algorithms, including ARIMA and LSTM, were used to ensure high forecasting accuracy. The models were designed to continuously learn and adapt to changing energy consumption patterns, ensuring long-term reliability and accuracy.
Outcome
By leveraging accurate forecasts, Husk negotiated appropriate contracts and scaled grid pricing effectively, resulting in $775,000 in additional revenue. The time series models provided 84% accurate energy consumption predictions, enhancing Husk’s ability to meet demand efficiently. Actionable insights from the forecasting models allowed Husk to negotiate and implement energy contracts aligned with regional consumption patterns, further improving profitability.
