IoT Based Load Forecasting for Reliable Integration of Renewable Energy Sources

Renewable energy resources have gathered substantial interest, and several nations are striving to use them as the dominant power resource. However, the power output from these energy sources is inherently uncertain due to their reliance on natural forces like wind, sunlight, tides, geothermal, etc. An accurate estimation of expected consumer load demand can assist with scheduling and coordination between various generating units, ensuring a consistent supply of power to consumers. Internet of Things (IoT) devices are becoming ubiquitous in all technological domains and making different kinds of data readily available. This data from heterogenous IoT sources can be combined and applied towards rapid, short-term load forecasting. This work proposes a Long Short-Term Memory (LSTM) based load prediction model that combines weather data, historical and current load demand to project the hour-ahead load demand. LSTMs are excellent for picking out patterns in time series data and learning long-term dependencies, allowing them to predict over a prolonged period. Using our LSTM model, we obtained a Mean Absolute Percentage Error (MAPE) of 0.62% on the hour-ahead forecast. We further enhanced this model using Wavelet Transforms (WT-LSTM) and observed an improvement of 16% over LSTM model. Both models performed significantly better than their equivalent Artificial Neural Network (ANN) model counterparts, with LSTM and WT-LSTM outperforming the ANN and WT-ANN by 50%, respectively. Short term load forecasts from models predicting on such streaming data from IoT sensors can be used to do rapid generator balancing, thus making the grid more reactive to changes and capable of providing a reliable power supply.