Evaluating LSTM Neural Networks for Energy Forecasting in CHP Plants: A Comparative Approach
This paper evaluates the performance of the Long Short-Term Memory (LSTM) Neural Network for forecasting energy production from combined heat and power (CHP) plants in Stuttgart, Germany. The dataset consists of high frequency time series data and exhibits multiple seasonal patterns. A key challenge addressed in this study is the presence of a systematic reporting delay, where the most recent four