Short-term electricity load forecasting of buildings in microgrids

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. (C) 2015 Elsevier B.V. All rights reserved.