###### Abstract  
A smart city is a sustainable and effectual urban center which offers a maximal quality of life to its inhabitants with the optimal management of their resources. Energy management is the most difficult problem in such urban centers because of the difficulty of energy models and their important role. The recent developments of machine learning (ML) and deep learning (DL) models pave the way to design effective energy management schemes. In this respect, this study introduces an artificial jellyfish optimization with deep learning-driven decision support system (A)ODL-DSSEM model for energy management in smart cities. The proposed A)ODL-DSSEM model predicts the energy in the smart city environment. To do so, the proposed A)ODL-DSSEM model primarily performs data preprocessing at the initial stage to normalize the data. Besides, the A)ODL-DSSEM model involves the attention-based convolutional neural network-bidirectional long short-term memory (CNN-ABLSTM) model for the prediction of energy. For the hyperparameter tuning of the CNN-ABLSTM model, the AJO algorithm was applied. The experimental validation of the proposed AJODL-DSSEM model was tested using two open-access datasets, namely the IHEPC and ISO-NE datasets. The comparative study reported the improved outcomes of the AJODL-DSSEM model over recent approaches. smart cities; energy management; decision support systems; deep learning; prediction models; hyperparameter optimization +
Footnote †: journal: _Sciences_  
Article
to assess the present conditions of the city (via data arising from sensor networks located in the metropolitan regions), and decisions should be made in accordance with particular goals and targets. This means the advancement of intensely linked substructures, emerging alongside the smart city atmosphere [3; 4]. Based on these methods, there exist decision support systems (DSSs) and computational methodologies. A DSS, broadly implemented in numerous sectors and fields to guide the automation of decisional functions, understands and interprets the diverse necessities to be encountered, considering the relative merits and demerits of the constituting components [5]. DSSs have been broadly researched and employed in a wide range of application zones, starting from clinical DSSs to management and business, including smart cities [6]. Figure 1 illustrates the process of energy management in smart cities. The energy in the smart city environment can be optimally managed to satisfy resource availability, system cost, geolocation characteristics, energy prices, regulatory constraints, environmental benefits, etc. The power deployment and impact of smart technologies, regarding the dynamic optimization of grid operations and resources, automation, analytics, and information exchange, are major difficulties for industrial units to understand the prerequisites for computational intelligence (CI) patterns in brilliant decision-support techniques [7; 8]. CI has several branches which are unconstrained to neural networks, such as expert systems, fuzzy systems, artificial immune systems, swarm intelligence [9], evolutionary computing, and numerous hybrid models, which are compositions of two or more branches. Additionally, CI is a successor of artificial intelligence (AI), and by means of future computing, approaches smart grid functions in energy management. Energy management is a global problem with significant consequences [10]. High power surges and environmental factors necessitate the transition of electric power grids and smart grids towards the direction of higher rational energy consumption (ECM). The suitable regulation of power generation and utilization promises its effective exploitation, which requires a smart grid to maintain consistent power transmission among users and producers for balancing the respective energy status [11].