The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-4/W3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W3-2020, 233–240, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-233-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W3-2020, 233–240, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-233-2020

  23 Nov 2020

23 Nov 2020

MACHINE LEARNING AND IOT FOR SMART GRID

M. Fouad, R. Mali, A. Lmouatassime, and M. Bousmah M. Fouad et al.
  • ENSAJ, National School of Applied Sciences of El Jadida, LTI Laboratory (Information Technology Laboratory), Morocco

Keywords: Machine Learning, Smart Grid, Internet of Things, RNN, LSTM, Forecasting, Prediction, Smart Meters

Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.