This paper presents a method to predict energy usage, based on weather conditions and occupancy, using a multiple linear regression model (MLR) in research office buidings. in this study, linear regression models of four research office sites in different regions of New Zealand were selected to show the capability of simple models to reduce margins of error in energy auditing projects. The final linear regression models developed were based on monthly outside temperatures and numbers of full time employees (FTE's). Comparing actual and predicted energy usage showed that the models can predict energy usage within acceptable errors. The results showed that each building should be investigated as an individual unit
Interpretable and scalable data-driven methodologies providing high granularity baseline predictions...
Accurate baseline energy models demand increase significantly as it lower the risk of energy savings...
Interpretable and scalable data-driven methodologies providing high granularity baseline predictions...
Offices and retail outlets represent the most intensive energy consumers in the non-residential buil...
This study was developed to predict energy consumption, based on the amount fruit stored and environ...
Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore,...
Energy consumption in commercial buildings has been growing substantially in recent years. Recently,...
Different ways to evaluate the building energy balance can be found in literature, including compreh...
Due to the character of the original source materials and the nature of batch digitization, quality ...
The present study describes the development of a multi-linear regression model to predict the effect...
This study was carried to improve the energy saving by investigating the influence factors that cont...
This study is directed at a more complete underst and ing of energy consumption in buildings and , b...
Reliable energy consumption forecasting is essential for building energy efficiency improvement. Reg...
Large building stocks should be well managed, in terms of ordinary activities and formulating strate...
The aim of this project is to build a counterfactual model across four energy types based on histori...
Interpretable and scalable data-driven methodologies providing high granularity baseline predictions...
Accurate baseline energy models demand increase significantly as it lower the risk of energy savings...
Interpretable and scalable data-driven methodologies providing high granularity baseline predictions...
Offices and retail outlets represent the most intensive energy consumers in the non-residential buil...
This study was developed to predict energy consumption, based on the amount fruit stored and environ...
Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore,...
Energy consumption in commercial buildings has been growing substantially in recent years. Recently,...
Different ways to evaluate the building energy balance can be found in literature, including compreh...
Due to the character of the original source materials and the nature of batch digitization, quality ...
The present study describes the development of a multi-linear regression model to predict the effect...
This study was carried to improve the energy saving by investigating the influence factors that cont...
This study is directed at a more complete underst and ing of energy consumption in buildings and , b...
Reliable energy consumption forecasting is essential for building energy efficiency improvement. Reg...
Large building stocks should be well managed, in terms of ordinary activities and formulating strate...
The aim of this project is to build a counterfactual model across four energy types based on histori...
Interpretable and scalable data-driven methodologies providing high granularity baseline predictions...
Accurate baseline energy models demand increase significantly as it lower the risk of energy savings...
Interpretable and scalable data-driven methodologies providing high granularity baseline predictions...