scholarly journals MACHINE LEARNING AND IOT FOR SMART GRID

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 233-240
Author(s):  
M. Fouad ◽  
R. Mali ◽  
A. Lmouatassime ◽  
M. Bousmah
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Grid ◽  
Power Distribution ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
Electricity Grid

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.

scholarly journals Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4881
Author(s):  
Zain Anwer Memon ◽  
Riccardo Trinchero ◽  
Paolo Manfredi ◽  
Flavio Canavero ◽  
Igor S. Stievano
Keyword(s):  
Machine Learning ◽  
Uncertainty Quantification ◽  
Electricity Market ◽  
Power Distribution ◽  
Principal Component ◽  
Distribution Networks ◽  
Monte Carlo Analysis ◽  
Support Vector ◽  
Uncertain Parameters ◽  
Power Distribution Networks

Today’s spread of power distribution networks, with the installation of a significant number of renewable generators that depend on environmental conditions and on users’ consumption profiles, requires sophisticated models for monitoring the power flow, regulating the electricity market, and assessing the reliability of power grids. Such models cannot avoid taking into account the variability that is inherent to the electrical system and users’ behavior. In this paper, we present a solution for the generation of a compressed surrogate model of the electrical state of a realistic power network that is subject to a large number (on the order of a few hundreds) of uncertain parameters representing the power injected by distributed renewable sources or absorbed by users with different consumption profiles. Specifically, principal component analysis is combined with two state-of-the-art surrogate modeling strategies for uncertainty quantification, namely, the least-squares support vector machine, which is a nonparametric regression belonging to the class of machine learning methods, and the widely adopted polynomial chaos expansion. Such methods allow providing compact and efficient surrogate models capable of predicting the statistical behavior of all nodal voltages within the network as functions of its stochastic parameters. The IEEE 8500-node test feeder benchmark with 450 and 900 uncertain parameters is considered as a validation example in this study. The feasibility and strength of the proposed method are verified through a systematic assessment of its performance in terms of accuracy, efficiency, and convergence, based on reference simulations obtained via classical Monte Carlo analysis.

scholarly journals A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1287 ◽  
Author(s):  
Alireza Vahabzadeh ◽  
Alibakhsh Kasaeian ◽  
Hasan Monsef ◽  
Alireza Aslani
Keyword(s):  
Power Distribution ◽  
Measurement Data ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
Bottom Up ◽  
High Penetration ◽  
Normal Behavior ◽  
Som Model ◽  
Energy Theft

This study proposes a fuzzy self-organized neural networks (SOM) model for detecting fraud by domestic customers, the major cause of non-technical losses in power distribution networks. Using a bottom-up approach, normal behavior patterns of household loads with and without photovoltaic (PV) sources are determined as normal behavior. Customers suspected of energy theft are distinguished by calculating the anomaly index of each subscriber. The bottom-up method used is validated using measurement data of a real network. The performance of the algorithm in detecting fraud in old electromagnetic meters is evaluated and verified. Types of energy theft methods are introduced in smart meters. The proposed algorithm is tested and evaluated to detect fraud in smart meters also.

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