*2.2. Adaptive Predictive Modeling*

Demand load forecasting complexities are influenced by the nature of the demand load, which is a result of consumer behavior changes, energy policies, and load type. The behavioral changes of energy consumers result in different energy usage patterns. For instance, building load type is determined by the hosted activities: commercial, residential, or industrial. Energy consumption in commercial and industrial buildings occurs in the light of routine activities that are derived from either uniform equipment operations or the implied consistency of organized human activities [25]. Demand load is affected by both exogenous (i.e., weather conditions) and endogenous (i.e., type of day) parameters [26,27]. With a stationary demand load, such as an office building load, energy consumption follows a specific pattern; hence, variability in energy consumption is not volatile.

Conversely, non-stationary buildings such as hotels have a high-frequency fluctuation in their energy demand sequence because of randomized operation conditions, such as varying occupancy levels. The changing pattern leads to poor prediction accuracy with an unscalable predictive algorithm. To mitigate such problems, conventionally, for grid load forecast, utility operators use manual methods that rely on a thorough understanding of a wide range of contributing factors based on upcoming events or a particular dataset. Relying on manual forecasting is unsustainable due to the increasing number of complexities of the prediction. Hence, the predictive model for load should be adaptive to the changing conditions.
