Predictive Load Management Using IoT and Data Analytics

The objective of this paper is to design and implement an Internet of Things (IoT) based energy data acquisition system that incorporates a Long Short Term Memory (LSTM) model for predicting household energy demand and optimizing energy consumption with load scheduling and shifting. To achieve this goal, the system feeds 7 days’ worth of energy consumption data, along with relevant features such as temperature, humidity, precipitation, and holiday information, into the LSTM model to predict the energy demand curve. The system categorizes loads into deferrable and non-deferrable loads. Based on these categories, the system applies load scheduling techniques to flatten out the demand curve and optimize energy consumption. In addition, the system shifts the deferrable loads from the grid to renewable sources during peak hours if available. This helps to reduce the burden on the main grid and promotes sustainability. Furthermore, the system takes into account the day-ahead hourly price-based tariff rate, ensuring that energy consumption is cost-effective. This is achieved with a prototype Predictive Load Management Device (PLMD). To relay information to the users, the system includes a web-based application made in Blynk that presents the information in a simple, easy-to-understand format. This reduces the complexity associated with different techniques of demand-side management (DSM) and makes it accessible to users with varying technical backgrounds. The web-based application allows users to monitor energy consumption patterns, view predictions generated by the LSTM model, and make informed decisions on energy consumption intuitively and straightforwardly.