## 2 Background
### Machine Learning  
The complexity of residential load forecasting lies in the significant volatility and stochastic nature of the load profiles. Many researchers worldwide are working towards addressing this complexity by developing an accurate forecasting technique that addresses this uncertainty. The statistical learning approaches are based on the predefined relationship between variables and require a smaller data set but whereas the more accurate and advanced machine learning algorithms require a big data set. In recent years, the rise of big data with machine learning makes it a potential solution to address load forecasting in a residential energy management system. Traditional methods tend to avoid such uncertainty by load aggregation to offset uncertainties, customer classification to cluster uncertainties, and spectral analysis to filter out uncertainties [13]. Therefore, many studies are carried out to evolve the current machine learning techniques to learn the uncertainty at the building level directly due to the many influencing factors.  
According to [PERSON], a data scientist at Johnson and Johnson, most machine learning systems require the ability to explain to stakeholders why specific predictions are made [14]. The accuracy and interpretability trade-off is typically considered when choosing a suitable machine learning model. Generally, there are two types of machine learning models, namely, black-box and white-box. Black-box models such as neural networks and gradient boosting models yield highly accurate predictions. However, their computational operation is difficult to understand. On the other hand, white-box models such as linear regression and decision trees, despite being much easier to interpret, produce less predictive capacity. In this research, an initial comprehensive multi-criteria analysis of the most common machine learning techniques in models such as linear regression, gradient boosting, decision tree, and neural network was performed to determine the best potential method optimised for load forecasting application.  
Machine learning approaches are generally used to address supervised and unsupervised learning problems. Since the proposed methodology aims to predict energy consumption, supervised learning is a more suitable option as its primary function is to model the value of the target variable based on the predictor variables. Machine learning and artificial intelligence techniques are used in a wide variety of applications, such as load forecasting [15], determining product quality [16], and fault quality [17]. Linear regression, decision trees, and neural networks are all examples of supervised learning. Despite the similarities, their computation principles are different. Regression analysis is a methodology that allows finding a functional relationship among dependent variables and independent variables [18]. For complex systems, such as the energy consumption in buildings, the regression analysis is considered as an iterative process, in which the outputs are used to diagnose, validate, criticise, and possibly modify the inputs [18]. In the decision tree approaches, an empirical tree represents a segmentation of the created data by applying a series of simple rules. Through the repetitive process of splitting, predictions are made, and the logic is usually comprehensible [19].  
On the contrary, the neural network is a class of algorithms loosely modelled on connections between neurons in the human biological brain, which is designed to imitate the natural nervous system information process and decision making [20]. There are many choices of neural network optimising architecture that significantly influences the performance of the model. This study proposes a novel deep neural network model by optimising the hyper-parameters to enhance neural networks' performances. A comprehensive literature review in the form of multi-criteria analysis (MCA) was carried out. The next section of the paper will critically highlight how the MCA analysis is performed and evaluated.