Load Monitoring

Load monitoring, encompassing both intrusive and non-intrusive methods, aims to accurately measure and disaggregate energy consumption from individual appliances or components within a larger system, such as a home or wind farm. Current research emphasizes developing efficient and accurate algorithms, often employing deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs), and graph neural networks (GNNs), to address challenges like limited labeled data and high computational costs. These advancements are crucial for improving energy efficiency in homes and buildings, enabling predictive maintenance in industrial settings like offshore wind farms, and facilitating more effective grid management through real-time load prediction and control.