An operation method for reducing energy consumption and an electronic apparatus thereof are provided. The operation method includes obtaining, by the electronic apparatus, weather forecast information, inputting, by the electronic apparatus, the weather forecast information to an artificial intelligence model for predicting an amount of power to be consumed by a first air conditioner, and displaying, by the electronic apparatus, the predicted power consumption amount of the first air conditioner output from the artificial intelligence model, wherein the artificial intelligence model is trained to obtain correlation information between a weather condition and a power consumption amount of an air conditioner, based on a weather history and operations of a plurality of air conditioners related to the weather history, and predict the amount of power to be consumed by the first air conditioner based on the correlation information and the weather forecast information.

Systems and methods for controlling building equipment based on an indoor air quality (IAQ) ventilation analysis of a building. One system includes a controller including memory and one or more processors configured to continuously collect IAQ data from one or more sensors within the building, estimate a plurality of outdoor airflow rates for an area of the building during a plurality of transient periods using the IAQ data as input, generate a time series outdoor airflow rate includes the plurality of estimated outdoor airflow rates, and modify a control strategy for the area of the building based on the time series outdoor airflow rate and a ventilation schedule for the area of the building.

A computer-implemented method for estimating the energy required for temperature control in a building. The method comprising a training phase on data from a plurality of buildings, adaptation phase to a target building, and estimation phase. The training phase comprises calculating a parameter k which summarizes the thermal characteristics of the building. Subsequently a computer based grey box model is trained with input data comprising the parameter k, indoor conditions, outdoor conditions, and energy consumed for each building. In the adaptation phase similar process is utilized for calculating the target building's the characteristic parameter k. In the estimating phase, the energy for temperature control is estimated based on the parameter k of the target building, indoor conditions, and outdoor conditions by using the computer trained mathematical model of the training phase. The temperature values used may comprise: measured or settings of indoor temperature, and measured or forecasted outdoor temperature.