Case Study: RWE Poland achieves lower balancing costs and improved demand forecasting with SAS Demand-Driven Forecasting

Forecasting optimizes energy production

RWE Poland, a national utility, needed to predict customers’ next‑day electricity demand to match day‑ahead generation and purchases but could not access intra‑day markets. Forecasting was hampered by fragmented, deteriorating customer data, growing smart‑meter volumes, and many weather, economic and usage variables, which drove costly balancing errors when relying on departmental rule‑of‑thumb estimates.

RWE implemented a centralized SAS analytical repository and SAS Demand‑Driven Forecasting, using data cleansing, integration, hierarchical forecasts and time‑series/causal models to produce consensus, machine‑driven predictions and automated gap reports. The result was materially better short‑, mid‑ and long‑term accuracy, reduced balancing costs, identification of technical and non‑technical losses, and clearer investment and marketing insights that improved operational efficiency and planning.

RWE Poland