Abstract
AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.
Highlights
With greater moves towards using machine learning in predicting building energy usage, there are many direct comparisons of learning techniques using the same data
In addition to investigating how the error that occurs in said predictions may be reduced through the use of data segmentation, such as if it is more accurate to model every single energy meter in a building and summate their predictions, or model the building as a whole
This specific paper focusing on the change in accuracy of building level energy use, by SVR, and the forecast range increase; and if error of long term predictions (Monthly) can be reduced to or bellow the level of the average error in short term predictions (Daily and weekly) through data segmentation
Summary
With greater moves towards using machine learning in predicting building energy usage, there are many direct comparisons of learning techniques using the same data. In addition to investigating how the error that occurs in said predictions may be reduced through the use of data segmentation, such as if it is more accurate to model every single energy meter in a building and summate their predictions, or model the building as a whole. This specific paper focusing on the change in accuracy of building level energy use, by SVR (support vector regression), and the forecast range increase; and if error of long term predictions (Monthly) can be reduced to or bellow the level of the average error in short term predictions (Daily and weekly) through data segmentation.
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