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With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment.

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In this paper we present a dynamic localization system, allowing a mobile robot ro evolve autonomously in a structured environment. Our system is based on the use of two sensors : an odometer and an omnidirectional vision system which gives a reference in connection with a set of natural beacoiw. Our navigation algorithm gives a reliable position estimation thank to a systematic dynamic resetting. To merge our data, we use the Extended Kalrnan Filter ( E m ) . Our proposed method allows us to treat eficiently the noise problems linked to the primitive extraction, which contributes to the robustness of our system. Thus, we get a reliable and quick navigation system which can answer the constraints of security and real time linked to the moving of the robots in an industrial environment. We give the experimental results obtainedRom a mission realized in an a priori known environment.