Short-term power consumption forecasting using neural networks with first- and second-order differencing

Electricity consumption forecasting is critical for efficient energy management and planning. Traditional time series models, such as ARIMA (AutoRegressive Integrated Moving Average), have been widely used due to their simplicity and interpretability. However, they often struggle with capturing the nonlinearity and complexity inherent in real-world data, especially in the presence of high seasonal variability. Recent advancements in machine learning, particularly long short-term memory (LSTM) networks, have addressed some of these limitations by leveraging neural network architectures capable of learning complex temporal dependencies. Nevertheless, both ARIMA and LSTM models can fall short in certain contexts, especially when dealing with abrupt changes and seasonal patterns. Recent research has focused on enhancing model sensitivity to these elements by incorporating first- and second-order variations, significantly improving predictive accuracy.