## 1 Introduction  
Energy efficiency is a general concern, from people to governments, since it yields efficient reserve funds, lessens ozone-deppleting substance outflows and lightens energy requirements [1]. In recent years, carbon dioxide (CO\({}_{2}\)) emissions from buildings have been on the rise after a reduction between 2013 and 2016. In 2019, CO\({}_{2}\) emissions reached a total of 10 GtCO\({}_{2}\), the highest level recorded [2]. The decrease in building energy consumption is important to alleviate greenhouse gas emissions and help to advance sustainable building practices [3]. Noteworthy changes to decrease building operating expenses and assets arrangement may be reached through cutting-edge control techniques. Some of these techniques utilize forecasts of unsettling influences to foresee future building behavior and select an ideal arrangement of activities [4].  
Forecasting is aimed at anticipating the future as precisely as possible, given the entirety of the data accessible, and is one of the most significant instruments for a proactive energy management system [5; 6]. Lately, much consideration has been paid to advancement of energy utilization in the smart grid. Demand-side management is one of the prominent functions in a smart grid that enables end-clients to alter their demand for energy through different methods [7] such as building energy management systems (BEMS). BEMS were created for the efficient control and supervision of building energy utilization, allowing a decrease carbon dioxide emissions [8; 9]. Over the past several decades, forecasting building energy consumption has gained importance with the development of BEMS [10; 11]. The dynamic occupants' behavior presents vulnerability issues that influence the performance of BEMS. To address this vulnerability issue, BEMS may implement forecasting as one of the BEMS modules [12].  
Forecasting of building energy use is the foundation for intelligent building performance through low energy and control techniques [13]. Building energy utilization has extensive energy reserve funds potential, but it requires an exact forecast since the prediction will directly influence the control techniques and the building's potential energy reserve funds [14]. Considering the aforementioned, the objective of this paper is to provide a review of the forecasting methods used to reduce energy consumption in buildings. The main contributions of this paper are:  
* Literature review of the existing studies for the forecasting of energy consumption in smart buildings, exposing their contributions and limitations.
* Analysis of the different types of methods used in the forecasting of energy consumption in buildings from multiples perspectives.
* Identification of the current state and future challenges for the forecasting of building energy consumption.  
This paper begins with Section 2 by describing the methodology used for the review. Section 3 presents the methods, objectives, input variables, horizon and metrics used to support the forecasting of energy consumption in buildings. Section 4 discusses previous studies, and Section 5 presents future challenges in building energy consumption forecasting. Finally, in Section 6, conclusions are presented.