Abstract
During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.
Highlights
Buildings represent a large portion of a country’s energy consumption and associated greenhouse gas emissions
This literature review aims to answer the question, how have artificial neural network (ANN) been applied to forecasting the energy use and demand in buildings? This is accomplished by answering questions regarding, how
How have such models been developed before deployment? What are the performances of such models? What new trends are emerging? Answers to such questions may help mitigate duplicate research efforts and provide a benchmark for the forecasting performance of the ANN models
Summary
Buildings represent a large portion of a country’s energy consumption and associated greenhouse gas emissions. Canadian residential and commercial/institutional sectors consumed approximately 27.3% of the country’s total secondary energy usage in 2013. Amongst both sectors, the energy needed for space heating, space cooling and hot water needs accounted for 21% of the overall total secondary energy usage [1]. The energy needed in order to maintain internal conditions within buildings accounts for a significant portion of the overall energy usage and greenhouse emissions. Over the past few decades, researchers have dedicated themselves to improving building energy efficiency and usage through various techniques and strategies. The forecasting of energy use in an existing building is essential for a variety of applications such as demand response, fault detection and diagnosis, model predictive control, optimization, and energy management
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