Building energy consumption forecasting procedure using ambient temperature, enthalpy, bias corrected weather forecast and outlier corrected sensor data

摘要

A procedure for forecasting building energy consumption by evaluating performance of variable base degree and variable based enthalpy models. Dynamic weights are computed for the variable base degree and variable based enthalpy models and used in making future energy prediction based on weather forecast data. The weather forecast data may be corrected for bias. The variable base degree and variable based enthalpy models may be calibrated based on outlier removed historic energy consumption data and historic ambient air temperature data.

主权项

1. A method of predicting energy consumption in a building, comprising:
receiving historic ambient air data; receiving historic energy consumption data associated with a building; calibrating, by one or more hardware processors, a variable base degree model based on the historic ambient air data and the historic energy consumption data; calibrating, by said one or more hardware processors, a variable based enthalpy model based on the historic ambient air data and the historic energy consumption data; receiving weather forecast data; running, by said one or more hardware processors, the variable base degree model with the weather forecast data to produce a first energy consumption prediction; running, by said one or more hardware processors, the variable based enthalpy model with the weather forecast data to produce a second energy consumption prediction; computing, by said one or more hardware processors, a first weight associated with the variable base degree model dynamically based on performance of the variable base degree model and performance of the variable based enthalpy model during a predefined time period; computing, by said one or more hardware processors, a second weight associated with the variable based enthalpy model dynamically based on performance of the variable based enthalpy model and the variable base degree model during the predefined time period; and combining, by said one or more hardware processors, the first energy consumption prediction and the second energy consumption prediction as a function of the first weight and the second weight.