A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model

Abstract

The electric load forecasting is extremely important for energy demand management, stability and security of power systems. A sufficiently accurate, robust and fast short-term load forecasting (STLF) model is necessary for the day-to-day reliable operation of the grid. The characteristics of load series such as non-stationarity, non-linearity, and multiple-seasonality make such prediction a troublesome task. This difficulty is conventionally tackled with model-driven methodologies that demand domain-specific knowledge. However, the ideal choice is a data-driven methodology that extracts relevant and meaningful information from available data even when the physical model of the system is unknown. The present work is focused on developing a data-driven strategy for short-term load forecasting (STLF) that employs dynamic mode decomposition (DMD). The dynamic mode decomposition is a matrix decomposition methodology that captures the spatio-temporal dynamics of the underlying system. The proposed data-driven model efficiently identifies the characteristics of load data that are affected by multiple exogenous factors including time, day, weather, seasons, social activities, and economic aspects. The effectiveness of the proposed DMD based strategy is confirmed by conducting experiments on energy market data from different smart grid regions. The performance advantage is verified using output quality measures such as RMSE, MAPE, MAE, and running time. The forecasting results are observed to be competing with the benchmark methods. The satisfactory performance suggests that the proposed data-driven model can be used as an effective tool for the real-time STLF task.