The transformation of conventional grid into Smart Grid (SG) requires strategic implementation of the demand-sensitive programs while considering the varying fluctuations in the consumers’ load. The core challenges faced by existing electric system are that how to utilize electrical devices, how to tackle large amount of data generated by end devices and how to meet energy demands of consumers in limited resources. This dissertation is focused on the energy management of residential sector in the SG. For this purpose, we have proposed the Energy Management Controllers (EMCs) at three levels: at home level (including the single and multiple homes), at building level and at regional level. In addition, cloud and fog based environments are integrated to provide on-demand services according to the consumers’ demands and are used to tackle the problems in existing electric system. At first level, heuristic algorithms based EMC is developed for the energy management of single and multiple homes in residential sector. Five heuristic algorithms: genetic algorithm, binary particle swarm optimization algorithm, bacterial foraging optimization algorithm, wind driven optimization algorithm and our proposed hybrid genetic wind driven algorithm are used to develop the EMC. These algorithms are used for scheduling of the residential load during peak and off peak hours in a real time pricing environment for minimizing both the electricity cost and peak to average ratio while maximizing the user comfort. In addition, the advancements in the electrical system, smart meters and implementation of Renewable Energy Sources (RESs) have yielded extensive changes to the current power grid for meeting the consumers’ demand. For integrating RESs and Energy Storage System (ESS) in existing EMCs, we have proposed another Home EMC (HEMC) that manages the residential sector’s load. The proposed HEMC is developed using the earliglow algorithm for electricity cost reduction. At second level, a fuzzy logic based approach is proposed and implemented for the hot and cold regions of the world using the world-wide adaptive thermostat for the residential buildings. Results show that the proposed approach achieves a maximum energy savings of 6.5% as compared to the earlier techniques. In addition, two EMCs: binary particle swarm optimization fuzzy mamdani and binary particle swarm optimization fuzzy sugeno are proposed for energy management of daily and seasonally used appliances. The comfort evaluation of these loads is also performed using the Fanger’s Predicted Mean Vote method. For increasing the system automation and on-demand availability of the resources, we have proposed a cloud-fog-based model for intelligent resource management in SG for multiple regions at next level. To implement this model, we have proposed a new hybrid approach of Ant Colony Optimization (ACO) and artificial bee colony known as Hybrid Artificial Bee ACO (HABACO). Moreover, a new Cloud to Fog to Consumer (C2F2C) based framework is also proposed for efficiently managing the resources in the residential buildings. C2F2C is a three layered framework having cloud, fog and consumer layers, which are used for the efficient resource management in six regions of the world. In order to efficiently manage the computation of the large amount of data of the residential consumers, we have also proposed and implemented the deep neuro-fuzzy optimizer. The simulation results of the proposed techniques show that they have outperformed the previous techniques in terms of energy consumption, user comfort, peak to average ratio and cost optimization in the residential sector.