## 1 Introduction  
The electric power industry plays an important role in the economic development of a country, and its decisive operation provides significant societal wellbeing. As reported in [1] the global energy consumption is increasing for sustainable advancement in society; therefore, the effectiveness of electricity consumption prediction needs to be improved [2]. As reported by the World Energy Outlook in 2017, the compound annual growth rate (CAGR) of electricity demand will have a global incremental rise of 1.0% in the period of 2016-2040 [3]. Another report presented in [4] described that residential buildings generally consume 27% of the total energy consumption, whereas buildings in the United States (US) consume 40% of their national energy [5]. Owing to high energy consumption levels in residential buildings, efficient management of their consumption is essential. Therefore, proper planning of energy is vital for energy saving, which is possible through effective energy consumption prediction models. The electricity prediction strategies for short-term horizons are categorized into four types: very-short, short, medium, and long-term [6; 7]. Short and very short-term predictions refer to minutely ahead predictions up to several days. Medium-term load prediction means one week ahead prediction or up to a year, whereas annual or several years' ahead prediction is called very long-term prediction. Each electricity prediction horizon has its own applications; however, in this study, we focus on short-term and very-short-term predictions [6; 8; 9; 10]. The major applications of short-term electric load prediction are power plant's reliable and secure operation, reliability and economic dispatch, and power system generation scheduling. Short-term load prediction ensures power system security, and it is an essential tool for the determination of the optimal operational state. Reliability and economic dispatch are also important applications of power systems in short-term horizons, wherein the abrupt variation of load demand fluctuates its reliability. This causes a power supply shortage if the load demand is underestimated, which makes it difficult to manage overload conditions and the quality of the overall power supply system. Another application of short-term load prediction is generation scheduling, which can be achieved through accurate load prediction to verify the allocation of operational limitations, generation resources, equipment usage, and environmental constraints. In the literature, several studies have been conducted for short-term load prediction, electricity load forecasting, electricity demand forecasting, electricity storage, and occupant behavior [11; 12; 13; 14; 15]. The mainstream electricity load prediction models are mainly grouped into two categories: statistical models and artificial intelligence models [16; 17]. Statistical models such as Auto Regressive Integrated Moving Average (ARIMA) [18], linear regression [19], Kalman filtering [20], and clustering [21; 22] etc. were used for load prediction in the early days. These models are effective in learning linear data but inadequate to learn the nonlinear complex electricity load. Besides this, the artificial intelligence models can learn nonlinear complex and linear electricity loads, which are further divided into shallow and deep structure methods. Shallow based methods include random forest [23], wavelet neural networks [24], support vector machines (SVMs) [25], artificial neural networks (ANN) [26], and extreme learning machines [27]. Shallow based methods perform well compared to statistical methods but perform poorly in feature mining. Hence, these methods require more features and selects strong features to enhance the prediction accuracy. Obtaining optimal feature extraction is a challenging task for these methods. Further, these methods have insufficient generalization ability over different datasets due to small hypothesis owing to less number of parameters.