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Forecasting the electrical appliance power consumption is a necessary and important part of the management of electrical power system, in order to assess people's penchant for using electricity. Even though several studies are focused on forecasting building consumption, less attention is given to forecasting the use of single appliances. Indeed, some of the energy needs of consumers may be relatively delayed or anticipated to obtain a better consumption profile while maintaining consumer comfort. This paper focuses on forecasting appliance power consumption using a non-linear autoregressive (NAR) neural network model. The results obtained on the UK-DALE public dataset demonstrate that NAR models are suitable for forecasting of energy consumption with a good accuracy. The proposed model obtained the best Mean Absolute Errors, compared with the LSTM, Autoencoder, Combinatory optimization, FHMM, and Seq2point techniques.

Stereo vision is a popular method for an artificial vision-based environment perception system used in various applications such as intelligent transportation. With two cameras, the disparity map is calculated to find the distance and depth of objects in front of a moving vehicle. The key element of the stereoscopic system is based on the sum of absolute differences (SAD) algorithm, which is the most repeated operation in the stereo matching subsystem; however, this algorithm requires a very intensive processing time, statistical analysis show that the SAD block can consume more than 80% of the overall processing time of the algorithm. In this paper we propose a highly efficient hardware architecture of the SAD algorithm for real time stereo matching, the proposed architecture is established by a hierarchical parallel architecture of the SAD block, and verified by simulation and successfully implemented in Cyclone IV field programmable gate array (FPGA), it provides a significant reduction of processing time and the performance of the stereo imaging system is able to achieve 30 frames per second of 640×480 resolution color images.

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