<p>Accurate electrical load prediction is essential for effective energy generation, preventing overloads and optimizing storage since residential electricity demand is rising annually due to infrastructure, construction and population growth. Conventional projection methods usually depend on mathematical models, which have limitations when identifying intricate patterns and connections in data. Although some conventional algorithms for machine learning are also yielding improved outcomes in a variety of domains, deep learning-based algorithms have been used to improve forecasting precision for power sources like electricity due to the unsteady and nonsmoothed patterns of household electricity consumption. Data of 29 months was gathered from 46 smart meters that were placed in the Bareilly district of Uttar Pradesh, India and concomitant meteorological data were obtained from the National Aeronautics and Space Administration's website. Following an extensive review and prior processing of the data, the efficacy of the algorithms is compared using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R-squared error (R²). The proposed model used for this study is Bidirectional Long Short-Term Memory. Furthermore, the two Bi-LSTM layers of the component use the aforementioned data as well as forward and backward time series patterns to generate predictions. Additionally, a performance comparison with the Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), Support Vector Machines (SVM), Gradient Boosting Regression (GBR), Convolutional Neural Network (CNN) and Linear Regression (LR) has been conducted with the objective to validate the findings of the proposed approach. It has been observed that BiLSTM performed more effectively than other machine learning techniques in respect to forecast precision; as a result, the evaluation metrics' values for MAE, MSE, RMSE and R² are 1.2896, 6.2896, 2.5079 and 0.9346, respectively.</p>