Hourly scaling down of an in-house annual energy demand estimation method: A novel approach for hourly heating consumption analysis

Accurately estimating a building's energy demand is fundamental for optimizing energy management and promoting energy efficiency. This study focuses on an in-house developed method for annual energy demand estimation, which relies on 30 key input parameters to predict energy consumption. While this approach is effective for long-term assessments, it lacks the temporal resolution required for applications that demand a more dynamic analysis, such as the creation of energy communities and smart energy systems. To address this limitation, an alternative methodology has been developed by scaling down an in-house annual estimation method and adapting it for hourly calculations, specifically for heating demand. In addition to comparing the results of the annual and hourly approaches, this study further investigates each individual contribution to heating demand - namely, heat transmission, ventilation losses, internal heat gains, and solar gains - by disaggregating them and scaling them on an hourly basis. This detailed analysis allows for a more precise representation of short-term variations in energy demand and highlights the limitations of aggregated annual estimates, to support the development of renewable energy communities, based on hour-ahead thermal and electric loads' estimation. The findings demonstrate the advantages of a higher-resolution approach and represent the first step in developing a flexible and robust tool capable of estimating both annual and hourly energy needs. Such a tool can be particularly useful for integrating renewable energy sources, optimizing demand-side management strategies, and supporting the development of energy communities, where precise energy profiling is crucial for improving efficiency and sustainability.