Short-term electricity load forecasting of buildings in microgrids

Abstract

Electricity load forecasting plays a key role in operation of power systems. Since the penetration of distributed and renewable generation is increasingly growing in many countries, Short-Term Load Forecast (STLF) of micro-grids is also becoming an important task. A precise STLF of the micro-grid can enhance the management of its renewable and conventional resources and improve the economics of energy trade with electricity markets. As a consequence of the highly non-smooth and volatile behavior of the load time series in a micro-grid, its STLF is even a more complex process than that of a power system. For this purpose, a new prediction method is proposed in this paper, in which a Self-Recurrent Wavelet Neural Network (SRWNN) is applied as the forecast engine. Moreover, the Levenberg–Marquardt (LM) learning algorithm is implemented and adapted to train the SRWNN. In order to demonstrate the efficiency of the proposed method, it is examined on real-world hourly data of an educational building within a micro-grid. Comparisons with other load prediction methods are provided.