The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings.

The Smart Grid (SG) is the technological development that incorporates digital technologies and advanced communication methods to determine and respond to variations in electricity consumption in order to revolutionize power distribution, transmission, and generation. In the conventional electrical grid, customers remain unaware to their energy usage patterns that not only results in energy loss but also money. The consumers’ usage and consumption standards need to be regulated in order to improve energy efficiency (EE). SG utilizes demand side management (DSM) for energy savings by use of various approaches like financial incentives, subsidized tariffs, and awareness to alter consumers’ energy demand. In smart environments (SE), Internet-of-Things (IoT) is evolving as a significant partner for resource and energy management. DSM in SG must take advantage of smart energy management system (SEMS) developed on smart meters (SMs) and modern technologies like IoT. Using SMs with IoT based technologies makes SEMS more effective in the SG. This paper offers design, deployment, implementation, and performance evaluation of an IoT based SEMS, including SMs as well as IoT middleware module and its related benefits. The proposed SEMS operates online and offers real-time (RT) load profiles (LPs) to customers and suppliers remotely. The customers’ LPs allow suppliers to disseminate and regulate their incentives as well as incite the customers to alter their energy consumption. Furthermore, these LPs serves as an input for developing numerous DSM approaches. The proposed solution is installed and evaluated at 4 different locations of Stylo Pvt. Ltd. Pakistan, which can communicate commands and observe the efficiency of electricity supplied by the utility. Moreover, the RT impact of using separate SM for automated control of heating, ventilation and air conditioning (HVAC) system is shown in terms of power consumption. The RT case study presents the efficacy of the proposed IoT-based-SEMS.

Growth of population and the inception of new devices every day comes with an incessant rise in energy consumption and has brought great challenges in terms of energy management at the consumer side. With the evolution of technology, smart meters (SMs) are not only considered merely as tools to measure energy consumption but act as a main resource of energy management systems. The application of SM spans over a wide range of advantages, including accurate billing data, information of utilization at the user end, the establishment of two-way communication and remote control of the user equipment. SM is the most essential element of a smart power grid that with the help of any smart energy management system (SEMS), assesses, measures, controls, implements and communicates power allocation, utilization, and consumption at both, single device, and network level. Data provided by the SMs is used by power supply companies to revolutionize power distribution and consumption through various techniques such as, non-intrusive load monitoring and demand-side management (DSM). For efficient data gathering and utilization, internet of things (IoT) is emerging as a key partner in the power industry leading to effective resource management. This paper provides presentation, deployment, and validation of an IoT based SEMS strategy and its related benefits to overcome challenges of energy management at consumer side. The presented SEMS incorporates various communication interfaces and protocols to integrate with any software-based smart solution. In this work, Entrack software is used for data gathering and analysis. As a proof of concept, the presented system is tested and implemented in 4 different buildings of a well-known private company in Pakistan, i.e., Stylo Pvt. Ltd. The case study analysis in this work shows the effectiveness of the presented IoT based SEMS.

The Smart Grid, a fusion of digital technologies and advanced communication methods, enables the transformation of power distribution, transmission, and generation by responding to fluctuations in electricity consumption. Conventional electrical grids often leave consumers unaware of their energy usage patterns, resulting in both energy wastage and financial loss. To enhance energy efficiency, the consumption patterns of consumers need regulation. Smart Grids efficiently employs Demand-Side Management (DSM) strategies, including peak clipping, load shifting, and consumer awareness campaigns, to optimize energy consumption and achieve energy savings. DSM’s synergy with smart meters and smart energy management systems (SEMS) emerges as a powerful trio in comprehensive energy conservation and optimization. The integration of SEMS with the Internet of Things (IoT), augmented by the advancements in 5G technology, emerges as a transformative paradigm. SEMS, operating within the IoT ecosystem bolstered by 5G connectivity, facilitates the instantaneous and efficient integration of IoT in SEMS, enabling real-time data collection, in-depth analysis, and data-driven decisions for optimal energy management. This empowers users with the ability to make data-driven decisions, yielding tangible outcomes in the form of efficacy, cost reductions, and fortified sustainability initiatives. We present a SEMS that amalgamates three microcontroller units to create a smart meter that is further integrated with a cloud-based middleware module and consumer API. Furthermore, it collects vital power metrics such as voltage, current, kWh, kW, and PF. Consumers can conveniently access this data in real-time via an API. Energy service companies can leverage this data for incentive programs and to motivate customers to optimize their energy consumption. These datasets serve as a foundational element for the development of diverse DSM strategies. This research work presents the deployment, and performance evaluation of an IoT-based SEMS that is implemented in diverse settings, including industrial, commercial, building, and warehouse setups. Moreover, as a test case, our evaluation extends to the specific conservation of energy within air-conditioning systems. The SEMS achieves significant energy conservation, with calculated savings ranging from 5% to 53%, showcasing its effectiveness in targeted energy management. The real-time data and case studies further demonstrate the efficiency of the presented work.

The increasing demand for energy and the growing environmental issues in Pakistan, require a movement to a more environmentally friendly and smarter energy infrastructure. This work provides the practical application of research which represents the deploying of a smart metering network in real time (RT) in Pakistan’s transition to more environmentally friendly and smarter energy systems. The work presents the design, implementation and the results of the operation of the smart metering deployment implemented by Gujranwala Electric Power Company (GEPCO). The presented system developed on a four-layer Internet of Things (IoT)-based architecture comprising of Energy Monitoring, Communication, Cloud Analytics, and Application layers. The smart meters (SMs) on the three classes of industrial loads (< 50 kW, 50–500 kW, > 500 kW) transmit RT data of the electrical parameters, including voltage, current, power factor, frequency, and consumption, to a centralized meter data management system (MDMS). This data enables the MDMS to support various functions such as automated billing, load profiling, fault detection, and power quality (PQ) analysis. Results of the case studies demonstrate that RT monitoring can assist in attaining a higher degree of grid visibility and operational responsiveness. One such case was the identification of the low power factor (PF) situations (below 0.7) which enabled the deployment of capacitors banks, resulting in measurable energy saving and cost saving in accordance with the mitigated exposure to PF penalties. For instant, in a one large-industrial scenario, PF improved from 67.5% ± 11.2 to 93.6% ± 2.4, corresponding to a significant Welch’s t large effect size, and with reduced day-to-day variability. Moreover, early detection of voltage imbalance, variance of frequencies, and daily peak load patterns were detected using the system. Using a conservative normalize-then-scale approach, a potential average PF uplift of approximately 1.4 percentage points across the industrial segment is projected under stated coverage and adoption assumptions. The results confirm that IoT-enabled smart metering can serve as a practical tool for demand side management (DSM), loss reduction and grid optimization. Finally, the study outlines key technical enablers, policy considerations, and institutional requirements for large-scale smart grid (SG) implementation and offers a replicable framework for developing economies pursuing energy system modernization.

The Internet of Things (IoT) has faced difficulties in its adoption because of security and data privacy issues that exist in the modern technological environment. Modern cybersecurity systems face multiple problems due to the fast-paced development of cyber threats. The research examines the issue where modern breach methods surpass traditional defense systems. Through federated learning, multiple clients train global models together by sharing machine learning without having to exchange actual data. The document emphasized that security functions as a vital component throughout End-to-End data security configurations. FL operates as an autonomous framework that provides improved data security through decentralized IDS training mechanisms implemented across separate connected devices. This paper analyzes Federated Learning methods and Virtual security protocols which demonstrate their vital role in modern networking infrastructure for establishing secure data transfer on unsecured internet networks. The paper explores the new difficulties that confront Machine Learning model implementations. These techniques help analyze large datasets from IoT devices that connect to the internet due to their effectiveness in processing internet-based application data. A Federated CNN model will serve as an effective system in the future to detect cyber threat identification.