\begin{table}
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Table 1: Advantages and Disadvantages of the Different Types of Forecasting Models Commonly Used in the Literature. In this paper, we perform a comparative study that includes a total of nine forecasting algorithms to investigate the impact of hybrid models and the incorporation of weather data on the overall accuracy of forecasting electricity demand in an NHS hospital. The strengths and main contributions of the study are summarised as follows:  
1. The use of a comparative study design and the inclusion of multiple forecasting algorithms allow for a robust analysis of the proposed technique. 2. The impact of hybrid models and weather data on electricity demand forecasting is assessed. 3. The use of an NHS hospital as the case study provides a relevant and practical application of the research, which could have important implications for hospital energy management and efficiency. \begin{table}
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**Algorithm** & **Advantages** & **Disadvantages** \\ \hline FBP & Simple and easy to implement. & Limited ability to capture complex patterns in data. Can perform poorly when there are abrupt changes or irregularities in the data. \\ \hline LSTM & Capable of capturing long-term dependencies in data. Can be computationally expensive and require large amounts of data for training. & Can be computationally expensive and require large amounts of data for training. Can be sensitive to the choice of kernel function. \\  & Able to handle nonlinear relationships between variables. & Can overfit if not properly regularised. \\ \hline SVR & Can handle nonlinear relationships between variables. & Can be computationally expensive and require large amounts of data for training. Can be sensitive to the choice of kernel function. \\ \hline RNN & Capable of capturing long-term dependencies in data. Able to handle nonlinear relationships between variables. & Can be computationally expensive and require large amounts of data for training. \\ \hline ANN & Can handle complex, nonlinear relationships between variables. & Can be computationally expensive and require large amounts of data for training. Can suffer from the vanishing gradient problem. \\ \hline ARIMA & Simple and widely used. Capable of capturing short-term dependencies in data. & Limited ability to capture long-term dependencies and nonlinear relationships between variables. Can perform poorly when there are abrupt changes or irregularities in the data. \\ \hline SARIMA & Extends ARIMA to handle seasonal patterns in data. & Limited ability to capture long-term dependencies and nonlinear relationships between variables. Can perform poorly when there are abrupt changes or irregularities in the data. \\ \hline LASSO & Able to handle high-dimensional data. Can perform feature selection and help with model interpretability. & Can be sensitive to the choice of regularisation parameter. May not perform well when there are nonlinear relationships between variables. \\ \hline MLP & Able to handle complex, nonlinear relationships between variables. & Can be sensitive to the choice of activation function. Can be sensitive to the choice of activation function. Can suffer from the vanishing gradient problem. Can be used for both regression and classification problems. & Can be computationally expensive and require large amounts of data for training. \\  & & Can suffer from the vanishing gradient problem. May require careful tuning of hyperparameters to prevent overfitting. \\ \hline \hline \end{tabular}
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Table 2: Advantages and Disadvantages of Specific Techniques/Algorithms Used in the Literature. The paper is structured as follows. Section 2 outlines the data used, Section 3 describes the methodology, Section 4 presents our obtained results, and finally, Section 5 concludes the paper and suggests potential future directions.