الفهرس | Only 14 pages are availabe for public view |
Abstract Forecasting in general is one of the hot topics helps in optimization and management. Load forecasting in specific is very interesting topic that helps in resources management and decision making especially in Smart Grid (SG). SG is a modern electric grid with a two-way flow of electricity and data between power utilities and consumers. Load forecasting helps in electricity management, such that prior knowledge of needed electricity helped in the amount of electricity needed to be generated. Various models have been developed to forecast electrical load whatever for short periods or long. Many of these models use their own techniques which are single models. Other models are combined models that combine forecasts from more than one model. Combined models either use linear techniques or nonlinear techniques for combining forecasts from different models. Linear techniques are easy to understand and implement. They use linear or equal weights for each of the contributing models and totally ignore the relationships between the participating models and consider only their contributions. So there is a considerable reduction of accuracy performance when two or more of the participating models are correlated. Nonlinear techniques are complex to understand and implement but they achieve better accuracy. Nonlinear techniques consider contributions of the participating models as well as the relationships among them, and that improves the performance accuracy when two or more of the participating models are correlated. In this thesis, a nonlinear weighted technique that combines forecasts of three of the participating models is proposed. At the beginning, forecasts from four models which are Random Forest (RF), Least Square Support Vector Machine (LSSVM), |