Short term load forecasting of distribution systems by a new hybrid modified FA-backpropagation method
Distribution systems as the final link between the production side and the consumers engage the most contributions to the unavailability of electrical services. In fact, in the operation of distribution systems, forecasting the short term load is a precious and critical task. With a more accurate load forecasting, the distribution system operation management would be improved and more economical trade off with the electricity market can be achieved. However, as the result of high nonlinearity and variation of the loads in distribution systems, short term load forecasting in these systems is hard and complex. In this regard, this paper proposes a new hybrid method based on firefly algorithm (FA) and artificial neural network (ANN) to reach a more reliable and accurate forecasting model. The proposed method makes use of both the learning ability of ANN and the powerful search ability of FA to create a nonlinear mapping between the input and output load pattern data. In contrast to the other evolutionary based on ANN training methods, this work preserves a good balance between ANN traditional training techniques such as back-propagation method and evolutionary random search ability of FA in a hybrid framework. Meanwhile, a new sufficient two-stage modification method is proposed for FA to improve its ability in both the local and global searches. The feasibility and satisfying performance of the proposed method is examined on the practical daily peak load of a part of Shiraz distribution system, Iran.
artificial neural network (ann) modified firefly algorithm (mfa) short term load forecasting (stlf)