Electric load forecasting using multivariate meta-learning : : doctoral thesis - Matijaš, Marin (M. Matijaš ; Fakultet elektrotehnike i računarstva, 2013)
ABSTRACT: Nowadays, the modern economies depend on electricity. Its production and consumption (load) have to be in equilibrium at all times since storing electricity, in a substantial quantity, results in high costs. Load forecasting is a task of predicting future electricity consumption or power load. The main goal, of load forecasting, is to reduce the load forecasting error. A reduction of the load forecasting error leads to lower costs and higher welfare. Besides keeping the power systems in balance and for power system control, load forecasting is important for power system planning; power market operation; power market design; and security of supply. Traditionally, load forecasting is solved, at a level of a particular task, with periodic aims of creating a more general framework for doing so. A general framework, for load forecasting, would be beneficial to most market participants in dealing with their heterogeneous load forecasting tasks. The deregulation of power systems created new markets and the advent of the Internet and new technology provided the opportunity for new functionalities and services which had more added values to the end customers. Mainly at the retail level, where profit margins are not high and, a small participant faces, in everyday operations, the problem of modelling taking into account a lot of uncertainty and different types of forecasting tasks. The general framework, for load forecasting, would be beneficial in modelling heterogeneous load forecasting tasks within a single forecasting system and would help the non-experts, in algorithm design, to choose the most suitable algorithm for their load forecasting task. In this doctoral thesis, I propose the use of meta-learning as an approach to electric load forecasting; this includes the framework for electric load forecasting model selection. I choose meta-learning because it is rooted in the idea of learning to solve better the problems of different characteristics. It is well-suited for load forecasting because it learns and evolves over time, just as the power systems live and evolve over time. So far, it has not been applied to either power systems engineering, or has it been applied to multivariate time-series forecasting. The proposed approach is based on the new method of using meta-learning for forecasting multivariate time-series. It is modular and it learns on two levels: on the meta-level; and the load forecasting task level. The proposed solution uses an ensemble of seven algorithms for classification at the meta-level and, seven other algorithms for forecasting at the load forecasting task level. On 3 load forecasting tasks, made from publicly available data, I show that, for a recurrent real-life simulation of one year of forecasting, a meta-learning system, built on 65 load forecasting tasks, returned a lowest load forecasting error when compared to 10 well-known algorithms. On the fourth task in load forecasting, similar results were obtained. However, these were for a shorter period of forecasting and by using an ex-ante approach. I propose, also, the approach for definition and selection of the meta-features for the time-series; this resulted in new meta-features applicable to the meta-learning of load forecasting. The approach resulted in the introduction of new meta-features of traversity; fickleness; granularity; and highest ACF. The proposed meta-learning framework is intended to be used in practice; therefore, it is parallelized; component-based; and easily extendable. -