# A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
## 1 Introduction  
Energy consumption forecasting models have become a fundamental piece of smart buildings to develop energy efficiency for a sustainable economy [1]. Forecasting models learn the utilization patterns from the chronicled energy consumption information to find the non-direct connection between the chronicled information and target consumption [2]. With the prevalence of building energy management systems in smart buildings, it is possible to store a massive amount of building activity information. It gives a chance to utilize a data-driven approach for building energy consumption forecasts [3]. The data-driven approach depends on time-series measurable investigations and artificial intelligence to analyze and forecast energy consumption [4]. Accordingly, such an approach does not need a broad arrangement of boundaries or detailed information in regards to the interior segments of the building [5]. Assuming that the fuel of data-driven models is data, it could be said that machine learning is the engine that drives them, making it one of the techniques that have grownthe most in data-driven models [6]. Due to this, approaches that can naturally produce new information and incorporate the additional information into the training phase are future opportunities in forecasting [7]. Building energy consumption forecasting can be separated into very short-term, short-term, medium-term, and long-term forecasting dependent on the forecast time horizons [8]. Short-term building energy forecasting is firmly identified with the everyday activity model of energy systems, which can give valuable direction to develop practical and energy-saving measures [9]. An accurate forecast of the short-term horizon is fundamental for further operation performance in smart buildings [10]. Recently, the short-term horizon has been studied quite a lot, applying a wide range of techniques, making it look like a well-known field of study. However, it is a significant field of study due to its specialized difficulty and financial effect of having a precise energy consumption forecast [11]. Some studies that have used data-driven strategy for short-term energy forecasting include: [PERSON] et al. [12] proposed a combined multiple short-term load forecasting models using an ensemble approach; some limitations in this study were that it does not correctly anticipate energy consumption on weekends, and the model was only tested in one building. [PERSON] et al. [13] proposed a building energy consumption forecasting model which utilizes long short-term memory networks and an improved sine cosine optimization algorithm; a limitation presented by this model was that the analysis of the effect of the characteristics on the power consumption value was not carried out. [PERSON] et al. [14] present an approach for building energy consumption forecasting with raw metered energy with missing qualities using a deep recurrent neural network. [PERSON] et al. [15] present an ensemble deep learning-based method to forecast energy demand by utilizing sequential conditions. The proposed method was assessed by utilizing a benchmark, a residential and non-residential building dataset. The studies mentioned above, as well as others for building energy consumption forecasting, use a one-step forecasting strategy, which consists of predicting the next step. Within the same category, a strategy that is not so commonly used but has shown promising results in different studies [16; 17; 18] is the multi-step forecasting strategy that allows multiple future steps to be predicted. Considering the aforementioned, this paper's objective is to present an energy consumption forecasting methodology that allows estimating energy consumption of the next 24 h at any hour of the day using a direct multi-step ahead forecasting strategy. The main contributions of this paper are as follows:  
* An alternative strategy that uses all the data and makes a forecast for the next 24 h at any hour of the day.