Identification of Loads Based on Historical Data

Abstract

To tackle the forthcoming electricity grid tension in Gothenburg, Sweden, this thesis introduces a two-step machine learning approach for managing electricity demand. Initially, consumers are grouped using K-Means clustering according to their past patterns of usage in order to identify categories with the most fluctuating behaviour. Subsequently, several Long Short-Term Memory (LSTMs)—Vanilla, Stacked, Bidirectional, and CNN-LSTM—are trained for predicting electricity demand for such high-impact consumer groups in response to real-time, varying price signals. These models are evaluated using mean absolute error (MAE), root mean square error (RMSE), and loss measures. Among these examined architectures, CNN-LSTM exhibits the most consistent and stable performance across test and prediction datasets. This approach minimises the data and computation needed for deep learning but allows for more customised forecasting. The proposed solution provides a resourceefficient and scalable solution for energy suppliers who wish to monitor changes in demand in response to price changes.