scholarly journals A new smart approach of an efficient energy consumption management by using a machine-learning technique

Author(s):  
Maha Yousif Hasan ◽  
Dheyaa Jasim Kadhim

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.

Author(s):  
M. Fouad ◽  
R. Mali ◽  
A. Lmouatassime ◽  
M. Bousmah

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.


Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3110
Author(s):  
Konstantinos V. Blazakis ◽  
Theodoros N. Kapetanakis ◽  
George S. Stavrakakis

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.


2021 ◽  
Vol 2 (3) ◽  
pp. p12
Author(s):  
Dauda Moses ◽  
L. C. Ezugu ◽  
Onwuka Immaculater Akudo ◽  
Isaac John Ibanga

The main purpose of this study was to assess equipment maintenance practices for effective electric power distribution in Adamawa State by Yola Electricity Distribution Company. Three research questions and three null hypotheses were formulated to guide the study. The study adopted descriptive survey research design. The population of the study was 69 consisting of 46 technicians and 23 supervisors in Yola Electricity Distribution Company. The whole population was used for the study. The instrument for data collection was a structured questionnaire developed by the researchers titled “Assessment of Equipment Maintenance Practices for Effective Electric Power Distribution Questionnaire (AEMPELPDQ)”. The instrument was validated by three experts and a reliability of 0.89 was obtained using Cronbach Alpha reliability method. Mean and standard deviation was used to answer the research questions while t-test was used to test the null hypotheses at 0.05 level of significance. The finding of the study revealed that Yola Electricity Distribution Company adopts monthly routine maintenance on 18 out of the 31 items listed equipment while quarterly routine maintenance is carried out on 11 of the 31 items. Weekly maintenance is adopted for only two (2) of the equipment; Out of the 31 items listed, 22 of the items are semi-annually maintained; while eight (8) of the items are annually maintained and only one (1) of the equipment (distribution board) is weekly maintained. Based on the findings, YEDC should ensure adequate inspection and supervision of equipment to prevent unwarranted breakdown that may affect effective distribution of electrical power; YEDC should ensure at least monthly routine maintenance is carried out on the equipment available to ensure effective usage.


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