## 1 Introduction  
Short-term load forecasting has a vital role in making fast and reliable decisions for distribution systems. Recent improvements in the smart grid, especially with the introduction of smart meters, have led to a significant increase in the amount of data. Analyzing these big data produced by smart meters and understanding diverse patterns of electricity consumption by customers can help to create more accurate load prediction models. Although these new opportunities have attracted the attention of distribution companies, many problems remain to be solved in the relevant literature. The increasing penetration of renewable energy sources, complex interactions between utility and customer, and time-varying load demand in the distribution grids adversely affect system stability [1]. To this end, developing a customer-based prediction model makes it necessary to direct short-term load forecasting applications from the distribution system level to the user level. However, adopting only customer-based approaches is not enough by itself to create the most accurate load estimation model. Weather, schedule of system maintenance and repair, seasonality, human behavior, and other external factors are also important issues to be taken into consideration in the prediction model [2]. Nowadays, daily energy consumption information of various customers is constantly shared with distribution companies thanks to advanced communication infrastructure and smart meters. This information is stored in local or cloud databases to be used in operational activities or online decision-making. In this way, users can benefit from an interactive platform that may result in stable electricity bills at a reasonable price while distribution companies can precisely determine the supply and demand ratio. Therefore, it is crucial for all distribution companies to integrate an efficient load-forecasting model and to extract beneficial features from customers' big data [3]. To grasp the problem of short-term load forecasting adequately in customer-level loads at the smart grid, despite the various solutions that have been conducted in the literature using numerous methods including advanced deep learning (DL) algorithms based on either an individual customer level or substation level as in Section 2, it seems obvious these days to acquire bidirectional flow of electricity consumption data via smart meters from all customers in the smart grid. This problem framework defines the customary workflow processes in the planning and operation of electrical systems including demand response, load forecasting, and energy scheduling that can build interactive platforms. Therefore, the existing solutions lack the development of a prediction model that acquires big data from all customers and provides more accurate consumption prediction using a hybrid DL algorithm and preprocessing enhancement. To the best of the authors knowledge, this issue of using advanced preprocessing techniques coupled with hybrid DL algorithm has not been studied previously. This study proposes advanced preprocessing techniques for load profile clustering and a hybrid DL algorithm, which is convolution neural network (CNN)--bidirectional long short-term memory (BLSTM), for short-term load forecasting of real big data sets in Turkey. Generally, load forecasting can be influenced by many factors such as time, weather, and the type of customer [4]. The three main types of load forecasting in terms of time horizon are short-term forecasting, mid-term forecasting, and long-term forecasting. Short-term load forecasting has been studied in the literature for a time interval ranging from an hour to a couple of weeks for different applications such as real-time control, energy transfer scheduling, economic dispatch, and demand response [4]. In this study, we investigate the short-term forecasting problem for potential demand response and real-time control applications for one-hour-ahead prediction applied by the utility company.