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Abstract: In new deregulated electricity market, price forecasts have become a fundamental input to an energy company’s decision making and strategy development process. However, the exclusive characteristics of electricity price such as non-stationarity, non-linearity and time-varying volatile structure present a number of challenges for this task. In spite of all performed research on this area in the recent years, there is still essential need for more accurate and robust price forecast methods. Besides, there is a lack of efficient feature selection technique for designing the input vector of electricity price forecast. In this paper, a new two-stage feature selection algorithm composed of modified relief and mutual information (MI) techniques is proposed for this purpose. Moreover, cascaded neural network (CNN) is presented as forecast engine for electricity price prediction. The CNN is composed of cascaded forecasters where each forecaster is a neural network (NN). The proposed feature selection algorithm selects the best set of candidate inputs which is used by the CNN. The proposed method is examined on PJM, Spanish and Ontario electricity markets and compared with some of the most recent price forecast techniques. [Copyright &y& Elsevier]

Abstract: This paper discusses the evolution of the SS-411 series digital sun sensor. The earlier SS-256 and SS-330 models have proven themselves in orbit on low-cost satellite missions. The SS-411 represents a further hardware revision with enhanced robustness and improved attitude estimation performance. To complement the latest hardware improvements, researchers in the Space Avionics and Instrumentation Laboratory (SAIL) at Ryerson University in Toronto have developed advanced signal processing routines compatible with the SS-411. These routines significantly improve the accuracy of the sensor''s estimation without increasing manufacturing complexity. With this advanced processing, the sensor maintains a accuracy of 0.11° over the entire field-of-view. [Copyright &y& Elsevier]