Abstract
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.
Highlights
INTRODUCTIONIn Smart Grid (SG) we are with a large amount of data through the addition of smart metering equipment and the exponential evolution of SG infrastructure
In Smart Grid (SG) we are with a large amount of data through the addition of smart metering equipment and the exponential evolution of SG infrastructure.This intelligent power grid enables two-way communication between the power grid and end customers, with emerging needs to monitor, predict, plan, learn and make decisions about energy consumption and production, in real time (Decebal Constantin Mocanu, 2016)
In this article we have clarified the complementarity, synergy and correlation between the field of Machine Learning (ML) for prediction, the Internet of Things (IoT) for information collection and the SG intelligent network which ensures the transfer of electrical energy in two-way and agile communication between all stakeholders in the network
Summary
In SG we are with a large amount of data through the addition of smart metering equipment and the exponential evolution of SG infrastructure This intelligent power grid enables two-way communication between the power grid and end customers, with emerging needs to monitor, predict, plan, learn and make decisions about energy consumption and production, in real time (Decebal Constantin Mocanu, 2016). With SG, the end consumer is informed almost in real time of the amount of energy consumed by each electrical device used, which contributes to the reduction in consumption All this is done through the interaction between the SG and the IoT (such as smart meters, sensors...) and energy prediction methods known as ML.
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