# A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
## 3 Methodology and Approach  
### Selected Forecasting Models  
For the selection of the models that were used to test the proposed strategy, the most popular data-driven models for forecasting demand [20; 21; 22; 23; 24; 25] in buildings were considered. In addition to this, models that have not been as widely used as temporal convolutional network and temporal fusion transformer were included; the reason for this was to see if these models that have been promising in other areas could bring better results. A brief description of each one is presented below:  
* **Multiple Linear Regression (MLR)** is a statistical method generally utilized for time-series forecasting. The fundamental thought behind simple linear regression is to attempt to discover the connection between two variables. For the situation where various independent variables are utilized to decide the value of a dependent variable, the process is called multiple linear regression [26].
* **Artificial Neural Network (ANN)** is planned to copy the fundamental architecture of the human brain, whose essential component is called a processing unit modeling a biological neuron. The network comprises a large number of these process units exhibited in layers, and process units in various layers are associated with each other via connections [27].
* **Random Forest (RF)** is a model where a bunch of decision trees are utilized to create the last output, utilizing a democratic plan. Each tree is initiated from an arbitrarily chosen preparing subset and additionally utilizing a randomly chosen subset of highlights. This suggests that the trees rely upon the upsides of an autonomously tested information dataset, utilizing similar dispersion for all trees [28].
* **Extreme Gradient Boost (XGBoost)**: the fundamental thought behind this method is to adapt successively in which the current regression tree is fitted to the residuals from the past trees. This new regression tree is then added to the fitted model to refresh the residuals. The principle of gradient boosting further improves the adaptability of the boosting algorithm by developing the new regression trees to be maximally related to the negative of the gradient of the loss function [29].
* **Long-Term Short Memory (LTSM)** comprises a memory block that is answerable for deciding the expansion and erasure of data through three entryways, namely input gate, forget gate, and output gate. The memory cell in the memory block recollects worldly state data about current and past timesteps [30].
* **Convolutional Neural Network (CNN)** comprises of four fundamental parts: convolutional layer, which includes maps of the information; pooling layers, which are applied to lessen the dimensionality of the convoluted element; flattening, which changes the information into a column vector; and a connected hidden layer, which computes the loss function [31].
* **Temporal Convolutional Network (TCN)** is a sort of convolutional neural network with a particular design that makes them appropriate for time series forecasting. TCN fulfills two primary standards: the network's output has a similar length as the input  
Figure 4: Data preparation steps for dataset formation.  
arrangement, and they avoid leakage of data from the future to the past by utilizing causal convolutions [32].
* **Temporal Fusion Transformer (TFT)** utilizes specific elements such as sequence-to-sequence and consideration-based temporal processing elements that catch time-fluctuating connections at various timescales; static covariate encoders that permit to condition temporal forecasts on static metadata; gating segments that empower skipping over unnecessary elements of the network; variable determination to pick important information at each time step, and quantile expectations to obtain output spans across all forecast horizons [33].