LSTM Based Forecasting of Power Consumption

Power consumption forecasting plays a critical role in effective energy management and resource allocation. However, accurate prediction of power consumption presents unique challenges due to the diverse nature of energy use patterns. This intricate interplay requires the application of advanced forecasting methodologies capable of effectively capturing the temporal dynamics and inherent non-linear relationships embedded within power consumption data across diverse time horizons. In accordance with these findings, this research delves into the complexities of the prediction of energy consumption by proposing a novel approach. We leverage long-short-term memory (LSTM) networks, a type of recurrent neural network (RNN), for long-term energy usage predictions due to their ability to model temporal dependencies. We utilized a polyregression model for short-term forecasting tasks, capitalizing on its effectiveness with smaller, non-cyclical datasets. The results demonstrate the superiority of LSTMs in handling complex relationships within power usage data. In contrast, the polyregression model, while achieving an acceptable R2 of 0.763 for smaller datasets, struggled with limited data points. These findings contribute significantly to the field of power usage forecasting by highlighting the effectiveness of long-Short-Term Memory networks in handling large, featurerich datasets and providing a robust and reliable approach for utilities and energy providers seeking to improve their load forecasting capabilities, leading to more efficient resource management and grid operation.