Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism.

ABSTRACT: The prediction of energy consumption in households is essential due to the reliance on electrical appliances for daily activities. Accurate assessment of energy demand is crucial for effective energy generation, preventing overloads and optimizing energy storage. Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques. To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. Followed by that, Modified Deep CNN-Bi-LSTM (Convolutional Neural Network and Bi-directional Long Short Term Memory) with attention mec

SUBMITTER: Binbusayyis A 

PROVIDER: S-EPMC11755059 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature