A General Variable Neighborhood Search heuristic for short term load forecasting in Smart Grids environment

The importance of short-term load forecasting has been increasing lately. Electric grids are changing from a centralized single supply model towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and very dynamic scenario. On the other hand, with deregulation and competition, energy price forecasting has become a big business. Bus-load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and the nonstationarity of loads are becoming worse due to the dynamics of energy prices. Hence, the load forecasting problem has become more difficulty and more autonomous load predictors are needed in this new conjecture. In this paper a novel method for load forecasting which combines the heuristics procedures Multi-Start (MS) and General Variable Neighborhood Search (GVNS) is described. The pseudo code of MSGVNS is presented and explained in details. MSGVNS was implemented in C++ via OptFrame framework. Our main goal is to evaluate the performance of this algorithm in a grid environment. Real data from an electric utility have been used in order to test the proposed methodology. The obtained results are fully described and analyzed.