Building planning energy consumption benchmark evaluation system based on regression analysis and data mining

First, this paper presents EMLBECPM for smart building planning and operations issues that use WSN to determine the best places to put various kinds of sensors and gateways and maximise energy usage within the bounds of connection, resources, security, and clustering coverage. Secondly, it uses support vector regression (SVR) and a nonlinear auto-regressive model with exogenous input (NARX) to predict power use for the next day, week, and month with a granularity of 15 minutes. The proposed autoregressive feature, temperature, and other situational relevant contextual data to heterogeneous time series exhibit distinct patterns on weekdays, weekends, and holidays. The outcomes demonstrate that the suggested technique is superior in optimising the building energy, accuracy, robustness, and generalisability. The simulation outcomes demonstrate the suggested energy optimisation ratio of 98.9%, efficiency ratio of 97.5%, energy monitoring ratio of 96.5%, prediction ratio of 95.4%, and error rate of 10.2% compared to other existing models.