# Prediction of Building's Thermal Performance Using LSTM and MLP Neural Networks
## 1 Introduction  
The residential sector makes an important contribution to energy consumption worldwide, representing more than the 40% of the total energy use in the European Union (EU) [1]. The EU has established several guidelines and directives to improve energy performance in buildings, such as 2010/31/EU (EPBD) [2] and 2012/27/EU [3], requiring that new buildings comply with nearly zero-energy buildings (NZEB) by 2030 [4] and to reach a decarbonized and highly energy efficient building stock by 2050. Therefore, energy efficiency in buildings is of great importance to the overall sustainability. Knowing exactly and precisely the energy consumption of a building is the first step to be able to optimize its energy performance. However, forecasting the building energy consumption is a difficult issue that many authors have investigated in recent years [5; 6; 7]. Different methods have been developed to predict building energy demand. Traditionally, dynamic simulation has been used successfully [8; 9; 10] becoming a suitable tool, that enables the assessment of building performance and the calculation of energy use. There are several building energy performance simulation (BEPS) tools available, such as TRNSYS [11], EnergyPlus [12] or DOE-2 [13]. However, the use of dynamic simulation tools requires the knowledge and control of many different parameters ofthe building, for example, envelope, materials properties, lighting, equipment, heating ventilation and air-conditioning (HVAC) systems and the behavior of the users, which makes the work of data collection arduous and increases the difficulty of obtaining exact results. The validation of these building energy simulation models is usually carried out with standard criteria such as the coefficient of variation of root mean square error (CV(RMSE)). Currently, these models are considered calibrated if they meet the criteria established by American Society of Heating Refrigerating and Air-Conditioning Engineers (ASHRAE) Guideline 14 [14], which states that a model is calibrated if its CV(RMSE) is below 15% monthly or 30% hourly. On the other hand, new tendencies in building prediction have emerged. Examples of this are the black box models, which stand out for their ability to train based on available data [15]. In this context, several data-driven models for building energy forecasting have been used more and more in recent years due to their robustness, resilience, strong yield and ease of implementation. Amongst them, the most popular data-driven approaches are the ones based on artificial neural networks (ANNs) [16; 17]. ANNs are specific mathematical models that attempt to replicate the way a human neural network proceeds. The main and most important feature of these models is the ability to learn; that is, from known data, they are able to extract a pattern and then extrapolate the results to new data. Thus, neural networks have a remarkable ability to model the non-linear relationships between inputs and outputs, and for their massive interconnectivity [10; 18; 19]. Two of the most used ANNs in the context of building energy prediction are multi-layered perceptron (MLP) [19; 20] and longshort-term memory (LSTM) neural networks [5; 21]. MLP models are well known for being composed of several layers connecting the inputs to the specific output [10]. They have been applied to numerous scientific fields such as environment [22; 23; 24], econometrics [19; 25], medical research [26], chemistry [27; 28] and even building energy [10; 29; 30]. On the other hand, LSTMs are more current models, but their application has also expanded to a great number of fields. This type of ANN is characterized by a recurrent neural network (RNN) architecture [21; 31]. RNNs differ from traditional feed-forward neural networks in that they have a hidden layer to consider connections with the previous values. This enables this type of neural network to model long-term dependencies; such as thermal inertia in buildings [32; 33; 34].