This paper presents a data-driven approach for estimating energy savings potential in commercial buildings by modeling the relationship between electricity demand and outdoor temperature. Designed for early-stage assessments, the method requires only minimal input: annual energy use, building type, and location, eliminating the need for detailed physical models or high-resolution data. Buildings are categorized by type, and for each category, typical demand-temperature profiles are developed for both pre- and post-implementation of advanced control systems. These profiles capture characteristic shifts in energy use as a result of operational improvements. To evaluate a new building, its energy profile is compared with these reference models, allowing the estimation of potential savings under similar control strategies. Validation with real-world data across diverse building types and climates demonstrates the method’s reliability and scalability, making it well-suited for screening large building portfolios.