A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation

In this article, we propose a novel ensemble technique with a multi-scheme weighting based on a technique called coopetitive soft gating. This technique combines both, ensemble member competition and cooperation, in order to maximize the overall forecasting accuracy of the ensemble. The proposed algorithm combines the ideas of multiple ensemble paradigms (power forecasting model ensemble, weather forecasting model ensemble, and lagged ensemble) in a hierarchical structure. The technique is designed to be used in a flexible manner on single and multiple weather forecasting models, and for a variety of lead times. We compare the technique to other power forecasting models and ensemble techniques with a flexible number of weather forecasting models, which can have the same, or varying forecasting horizons. It is shown that the model is able to outperform those models on a number of publicly available data sets. The article closes with a discussion of properties of the proposed model which are relevant in its application. Keywords: Ensemble techniques, Power forecasting, Multi model ensembles, Combining forecasts, Model selection, Time series, Data mining 1. Introduction During the past decade, there has been a tremendous growth of the installed capacity of various forms of renewable energy generation. Wind turbines and photovoltaic powerplants contribute substantially to the new mix of energy, which consists of both nonrenewable and renewable energy power plants. Most renewable energy sources have intermittent generation characteristics, i.e., the amount of generated power highly depends on the weather situation and it cannot be regulated the way it is possible with traditional power plants. In order to guarantee grid stability, the power generation and load in the grid have to be balanced, as the intermediate storage of electrical energy is both inefficient and expensive. Intelligent Embedded Systems Homepage: http://www. Depending on the forecasting horizon, the forecast is of interest to different actors in the field, e.g., network operators, power plant operators, or electricity traders. Having an accurate power forecast, the technical and financial risks for all market participants can be reduced. The power forecasting process typically takes place in two steps: 1. A meteorological forecast for the desired area (the location of the renewable energy power plant) is computed. This forecast is called numerical weather prediction (NWP). In this article, we focus on the second step of the forecasting process, i.e., we assume the NWP as given.