## 1 Introduction  
One of the upcoming features of load analytics in the smart grid (SG) is to analyze overall load on the grid and determine dynamic pricing schemes so as to redistribute the consumption load in a balanced way. Using advanced metering infrastructure (AMI) smart meters, utilities are now capable of recording electricity usage on a much more frequent basis (e.g., every 15 min) and enabling all consumers, who previously had bulk usage meters, to be introduced to real-time pricing programs that better reflect differences in the electricity cost. Furthermore, real-time pricing such as time-of-use (ToU) is more favorable than existing fixed pricing from the perspective of efficient power scheduling, demand-side management (DSM), and grid safety, such that existing fixed pricing is expected to be replaced with real-time pricing in the near future [1]. ToU pricing can be either fixed (f-ToU), where the price for each of the broad blocks of hours is predetermined or dynamic (d-ToU), where the low, normal (or medium), and high price periods vary every day. Studies have shown that such a dynamic rate program could greatly affect the electricity usage pattern of consumers [2]. On the other hand, real-time pricing increases the need to detect and prevent malicious consumers that tamper with the smart meter readings to steal electricity, with the primary intention of paying a lower tariff while consuming the same or a higher amount of electricity in the SG. This kind of tampering with the smart meter represents a major problem for utility companies and is a part of non-technical loss (NTL). As we notice in the following literature review, most of the existing schemes consider only energy consumption and do not consider ToU pricing schemes or other external factors for NTL theft detection algorithms. This is the basis of the central idea of our paper to consider the ToU scheme for smarter theft detection. In this paper, we consider only non-technical loss (NTL) theft, which primarily refers to data tampering in the smart meter to minimize the tariff for the malicious user. The literature review (including survey papers [3; 4; 5; 6; 7; 8; 9; 10]) shows that techniques used for detecting theft or abnormal usage activities in the SG take note of the daily historical electricity consumption pattern of that consumer. Machine learning (ML)- or artificial intelligence (AI)-based approaches automate the detection of theft in the SG using various techniques such as decision trees [10], artificial neural networks (ANN) [11; 12], support vector machine (SVM) [13], gradient boosting [14; 15], clustering [16], deep convolution neural networks [17], and so on. From the literature review, it is also noticeable that conventional theft detection is mainly based on energy consumption and not the pricing scheme. However, electricity consumption behavior is highly influenced by external factors such as time of use price [2], weather conditions [18], weekday/weekend, and so on. When these factors are considered, time periods with high and low theft probabilities can be estimated. As a result, in order to adapt to the layer of complexity introduced in the modern SG due ToU pricing, the theft cases need to be revised. Most importantly, in order to study ToU-based theft cases, we need granular datasets. Currently, the dataset used in [17] is one of the first public electricity consumption datasets with realistic labelled theft cases, released by the State Grid Corporation of China (SGCC). However, the sampling rate is one per day, meaning, only the total daily energy usage of the customers has been provided, so the SGCC data are not eligible for theft detection. Fortunately, the ToU-based dataset of the Low Carbon London project solves this problem. However, the only downside is the lack of real theft cases. Therefore, like many other existing studies in the literature, we are limited to experimenting with synthetically generated theft cases in this paper. However, this issue can certainly be addressed in some form in our future work.