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

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Original abstract

This paper presents a smart, scalable architecture that integrates Internet of Things (IoT) technologies and Deep Learning models to improve the accuracy and adaptability of energy consumption forecasting in residential environments. The proposed system is designed to support efficient data acquisition, storage, and analysis in dynamic home contexts, where consumption is influenced by multiple temporal, environmental, and behavioral variables. The system's foundation is a comprehensive IoT architecture developed for robust data collection. This infrastructure includes high-precision sensors to monitor power consumption across three phases, environmental sensors to capture weather variables like temperature and humidity, and occupancy detection mechanisms that infer human presence through smart device activity. Furthermore, a dedicated Android application facilitates the calibration of household appliance energy usage, enabling the identification of specific devices contributing to consumption fluctuations. Data is transmitted in real-time using the low-bandwidth MQTT (Message Queuing Telemetry Transport) protocol, managed via RESTful API services, and stored in JSON format within a highly scalable MongoDB NoSQL database, chosen for its big data capabilities. The predictive core of the system is a sophisticated neural network that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), specifically employing LSTM/GRU blocks, to effectively extract spatiotemporal patterns and capture long-term dependencies in timeseries data. The model architecture consists of six hidden layers with 2048 fully connected neurons each and is trained using the Mean Absolute Error (MAE) as the loss function with an Adam optimizer. The model incorporates a wide range of contextual factors, such as time of day, day of the week, holidays, weather conditions, and occupancy. Critically, it also explores user specific behavioral indicators, such as the presence of specific individuals, to achieve a more granular and accurate prediction. Evaluation was conducted on a real-world dataset collected over a two-month period, split into 80% for training and 20% for testing, with five-fold cross validation to prevent overfitting. The model demonstrated remarkable accuracy, achieving a Mean Absolute Error below 5% under diverse conditions. The system is designed for scalability, making it adaptable for larger applications such as residential communities or smart grid energy management. Future work will focus on enhancing model generalization by incorporating larger datasets over extended time frames and exploring the conversion of energy consumption data into images to further leverage the pattern recognition capabilities of CNNs.

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