Energy Usage Forecasting Model Based on Long Short-Term Memory (LSTM) and eXplainable Artificial Intelligence (XAI)

Abstract

The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to financial consequences for the company, since it will generate a high cost of energy production. Therefore, in this study, we proposed an energy usage forecasting model and parameter analysis using long short-term memory (LSTM) and explainable artificial intelligence (XAI), respectively. A public energy usage dataset from a steel company was used in this study to evaluate our models and compare them with previous study results. The results showed that our models achieved the lowest root mean squared error (RMSE) scores by up to 0.08, 0.07, and 0.07 for the single-layer LSTM, double-layer LSTM, and bi-directional LSTM, respectively. In addition, the interpretability analysis using XAI revealed that two parameters, namely the leading current reactive power and the number of seconds from midnight, had a strong influence on the model output. Finally, it is expected that our study could be useful for industry practitioners, providing LSTM models for accurate energy forecasting and offering insight for policymakers and industry leaders so that they can make more informed decisions about resource allocation and investment, develop more effective strategies for reducing energy consumption, and support the transition toward sustainable development.