Improving prediction performances by integrating second derivative in microgrids energy load forecasting

Accurate forecasting in time series data is crucial, especially in the energy sector, where prediction precision significantly influences decision-making and operational efficiency. This study investigates the efficacy of integrating second derivative data into forecasting models for energy consumption. We employ four distinct energy consumption time series datasets, each exhibiting varied characteristics and trends. The core of our methodology is the innovative incorporation of second derivative data to improve the accuracy of energy forecasting. This approach is applied to two widely recognized forecasting algorithms: Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). Our research introduces the second derivative of energy data as an additional input for these algorithms. This supplementary feature is used to provide deeper insight into the acceleration and deceleration trends in energy consumption, aspects often overlooked in standard models. We compare the performance of these enhanced models against their traditional counterparts, which do not utilize second derivative data. The results demonstrate a significant improvement in forecasting accuracy, particularly in peak regions, for both LSTM and XGBoost models with the inclusion of second derivative data. This research holds broad practical applications, notably in energy management systems and smart grid technologies, where it can contribute to more efficient energy distribution decisions.