## 1 Introduction  
On a customer service platform, the physical power system along with information and communication technology that link together heterogeneous devices in an automated fashion to improve the parameters of interest is a smart grid (SG) (refer to Figure 1 [1]). It is more likely that the SG will integrate new communication technologies, advanced metering, distributed systems, distributed storage, security and safety to achieve considerable robustness and reliability [2; 3; 4].  
Two-way communication is one of the key enablers that turns a traditional power grid into a smart one, based on which optimal decisions are made by the energy management unit [2]. In this regard, many demand-side scheduling techniques are proposed [5; 6; 7; 8]. However, there exists sufficient challenges prior to scheduling techniques in terms of stochastic information schemes to predict the future load. Thus, with the growing expectation of the adoption of SGs, advanced techniques and tools are required to optimize the overall operation.  
Day-ahead load forecasting (DLF) is one of the fundamental, as well as essential tasks that is needed for proper operation of the SG. On another note, accurate load forecasting leads to enhanced management of resources (renewable and conventional), which in turn directly affects the economies of the energy trade. However, in terms of DLF, the SG is more difficult to realize due to lower similarities (high randomness due to more load fluctuations) in the history load curves as compared to that of long-term load forecasting. In the literature, many attempts have been made to develop an accurate DLF model for SGs. For example, a bi-level DLF strategy is presented in [9]; however, this strategy is very complex in terms of implementation, which leads to a high execution time. Similarly, another load forecasting model based on a Gaussian process is presented in [10], which is not complex in terms of implementation; however, this model pays the cost of accuracy to achieve relatively less execution time. The model proposed in [11] focuses on day-ahead load forecasting in energy-intensive enterprises; however, this model is very complex, and thus, its execution time is relatively on the higher side.  
As mentioned earlier, the day-ahead load of an SG shows more fluctuations as compared to its long-term load. Accurate DLF model development with a fair enough execution time in these SGs is thus a highly challenging task. Alternatively, DLF accuracy enhancement may be achieved to some extent, however, at the cost of execution time. Therefore, we focus on the development of an accurate enough DLF model with a fair enough execution time for SGs. Our proposal consists of three modules: the data preparation module, the feature selection module and the forecast module. The first module normalizes and then encodes the input historical load data. This encoded information is sent to the feature selection module, where redundant and irrelevant features are removed from the input load data. It is worth mentioning here that in the feature selection module, we use our modified version of the famous mutual information technique (a detailed discussion is provided in Section 3.2). The selected features are sent to the ANN-based forecast module, which uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. In simulations, we compare our newly-proposed model with an existing one in terms of forecast accuracy and execution time. Results justify the applicability of our proposition.  
The rest of the paper is organized as follows. Section 2 contains relevant DLF contributions from the research community. Section 3 provides a brief description of the proposed ANN-based DLF model for  
Figure 1: A smart grid (SG).  
SGs. Section 4 provides the discussion of the simulation results, and Section 5 ends the research work with conclusions and future work.