CASE STUDY
ENERGY & UTILITIESAI demand forecasting for a UAE grid utility — 12% forecast error reduction
Result
AED 2.3M annual savings identified
Timeline
10 weeks
Sector
Energy & Utilities — UAE
The Context
A UAE grid distribution operator responsible for managing electricity supply across a high-demand urban and industrial service area was experiencing persistent over-procurement of power from the national grid. The root cause was consistent overestimation in their AI demand forecasting model — a production system that had been built 4 years prior and never retrained on updated load patterns.
The organisation had internal data science capability but lacked the AI/ML expertise to diagnose and fix the forecasting model. Emergency procurement costs — the market premium paid for unplanned short-notice grid capacity — were running significantly above sector benchmarks.
The Challenge
The existing forecasting model was a gradient boosted ensemble trained on 2019 consumption data. Since 2019, the service area had changed significantly: two large industrial zones had expanded, residential density had increased, and post-pandemic return-to-office patterns had altered the commercial load curve.
The model had not been retrained or recalibrated. Feature engineering had not been updated to reflect the new load composition. As a result, the model was systematically overestimating peak demand in commercial zones while underestimating industrial baseline load — leading to procurement decisions that were consistently misaligned with actual draw.
Our Approach
We conducted a 10-week AI forecasting improvement engagement covering three phases:
Model Audit
Full assessment of the existing model: training data vintage, feature engineering, validation methodology, and production monitoring infrastructure. We identified 8 specific failure modes — including the feature drift issue, the absence of a post-2022 retraining dataset, and the lack of out-of-sample backtesting against recent actuals.
Model Rebuild
Retrained the core forecasting model using 3 years of updated consumption data. Extended the feature set to include industrial zone expansion data, updated commercial occupancy patterns, and EV charging load estimates. Introduced a two-tier forecasting architecture: a zone-level model for granular demand prediction and an ensemble aggregation layer for overall procurement signalling.
Validation & Integration
Backtested the updated model against 6 months of actual consumption data. Validated procurement signal accuracy across 4 load categories. Integrated new model into the existing SCADA-adjacent procurement workflow. Documented the retraining pipeline so the internal team can update the model quarterly.
The Outcome
12%
reduction in forecast error (MAPE) vs. previous production model
AED 2.3M
in emergency procurement costs identified for annual reduction
18%
improvement in peak-period procurement accuracy
10 weeks
from audit kickoff to production deployment
The client team was trained on the retraining pipeline. A quarterly model review process was documented and handed over. The internal data science team now owns the model.
"The model they built didn't just improve our numbers — it came with a process we can actually maintain ourselves. That's rare."
— Head of Asset Management, UAE Grid Operator
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