An Intelligent and Scalable IoT-Based Architecture for Residential Energy Consumption Forecasting Using Deep Learning

Network-Aware Energy Management in IoT Systems: Benchmarking Time Series and Deep Learning Methods for Network Resource Optimization

Innovative Machine Learning and the Internet of Things for Smart Buildings with Low Energy Use: A Revolutionary Learning Optimization Framework

The increasing urbanization and energy demand necessitate state-of-the-art building management systems that can maximize energy efficiency while maintaining tenant comfort. Internet of Things (IoT) smart buildings constantly log data on occupancy, operations, and the surrounding environment. In order to derive useful insights from this mountain of data, sophisticated analytical frameworks are required. Modern optimization frameworks that integrate Machine Learning (ML) and the Internet of Things (IoT) lessen the energy consumption of smart buildings without sacrificing performance, sustainability, or user happiness. In the proposed system, sensors that are part of the Internet of Things track things like illumination, temperature, humidity, air quality, occupancy, and equipment performance. Machine learning algorithms examine the data sent by these networked devices, which might be located in a centralised or edge-based analytics platform.

Hybrid Digital Twin and IoT Framework for Smart Building Energy Optimization

A Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization for scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making is proposed.

Anomaly-aware electric vehicle charging and battery storage management using smart-meter data and a GCN-BiLSTM autoencoder

This research develops a smart meter-based residential energy management system that uses advanced deep learning to detect anomalies in electricity consumption data and optimize power usage in homes with electric vehicles and battery storage. The proposed framework addresses the limitations of traditional smart meters, which usually measure and transmit power consumption but do not actively identify abnormal readings or support intelligent energy scheduling. Smart meter data from 900 households are used to design and evaluate the model. The anomaly detection module is based on an autoencoder that integrates Graph Convolutional Networks and Bidirectional Long Short-Term Memory networks. This structure enables the system to learn both the relational and time-dependent behavior of household energy consumption. Missing values and abnormal readings are detected by comparing original and reconstructed consumption patterns. The corrected smart meter data are then applied to a multi-objective power management strategy that controls EV and battery charging/discharging, reduces peak demand, lowers grid energy consumption, and improves the use of renewable energy sources. The results show that the proposed method achieves high anomaly detection performance and provides better energy cost reduction than conventional approaches. Therefore, the study offers a practical framework for reliable, economical, and sustainable residential energy management.

IOT-BASED ENERGY MANAGEMENT SYSTEM IN ELECTRIC VEHICLES USING OPTIMIZED DEEP LEARNING

Electric vehicles are becoming the backbone of smart mobility in smart city applications because of their potential to reduce carbon footprints. In this research, an IoT-based energy management system for EVs by combining the harbor seal whisker optimization (HSWO) and the improved Elman spike neural network (IESNN) has been proposed. The proposed method uses voltage and current sensors on the battery and supercapacitor to transmit real-time energy parameters to the Blynk IoT platform for remote control and monitoring. The proposed IESNN method is used to predict system power demand. Moreover, HSWO is used to tune the network's weight parameters to improve prediction accuracy. IoT integration enables predictive maintenance and real-time data monitoring, enabling users to remotely assess motor performance, energy usage, and battery health. Compared with existing techniques, HSWO-IESNN improves SoC by 11.9%, 9.3%, 7.3%, 5.6%, and 4.4% over IWHO-DL, SCSO-RERNN, EMCABN-ROA, MRA-SDRN, and FBPINN-SAO, respectively.

Descriptor: Batteryless, Light- and Energy-Aware IoT Environmental Sensing Dataset (Li-IoT)

Research on low-power Internet of Things (IoT) systems has gained significant momentum within the broader context of green and sustainable IoT. In this setting, batteryless (BL) IoT has emerged as a promising solution to reduce maintenance costs and environmental impact by eliminating the need for battery replacements. As large-scale IoT deployments for monitoring and sensing applications continue to expand, important challenges arise in the design and operation of sustainable BL IoT networks, including long-term reliability, performance evaluation, and the analysis of energy-harvesting behavior under real-world conditions. To help address these challenges, this article presents a comprehensive dataset capturing the behavior of BL IoT devices deployed in an indoor environmental sensing network. The dataset comprises 100 days of measurements collected from sensors installed throughout an office building and includes data from two types of IoT devices: 1) plugged-in (PI) sensors with continuous power supply; and 2) BL sensors powered exclusively by energy harvested from indoor light. This dual-device deployment enables direct comparison of sensing performance, reliability, and energy dynamics between powered and energy-harvesting systems. The final dataset contains over three million samples collected from 28 sensors (14 PI and 14 BL) deployed across approximately 300 m<inline-formula><tex-math notation="LaTeX">${}^{2}$</tex-math></inline-formula> of office space spanning nine rooms. The dataset provides a valuable resource for the systematic investigation of BL IoT systems, enabling rigorous analysis of deployment strategies, spatial–temporal sensing dynamics, energy-harvesting behaviors, system performance, and long-term sustainability considerations in next-generation self-powered IoT sensing networks.</p> <p><bold>IEEE SOCIETY/COUNCIL</bold> Communications Society (COMSOC)</p> <p><bold>DATA TYPE/LOCATION</bold> Comma Separated Values (CSV); KU Leuven, Belgium</p> <p><bold>DATA DOI/PID</bold> <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.21227/TF0N-SB66">10.21227/TF0N-SB66</ext-link>