Cooling Load Forecasting Through an Innovative Environmental Awareness Module

Accurately predicting cooling load is a significant stride towards enhancing energy efficiency. This prediction relies on a multitude of factors. This paper explains the idea behind Environmental Awareness, elucidating a comprehensive methodology for selecting the most fitting forecasting technique for the Weather Awareness Module (WAM). Exponential Smoothing and the Nonlinear Autoregressive Neural Network with External Input (NARX) are analyzed, utilizing time series data pertaining to specific humidity and dry-bulb temperatures within the Boughezoul region. This work underscores the significance of data analysis, and the findings showcase that the NARX approach is particularly suitable for short-term forecasting objectives.