Data mining with neural networks - an applied example in understanding electricity consumption patterns.

2011 ◽  
pp. 240-303 ◽  
Author(s):  
P. Brierley ◽  
B. Batty
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Electricity Consumption ◽  
Consumption Patterns

scholarly journals Classification of electricity consumers using artificial neural networks

2019 ◽  
Vol 32 (4) ◽  
pp. 529-538
Author(s):  
Dragana Knezevic ◽  
Marija Blagojevic
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Electricity Consumption ◽  
Test Dataset ◽  
Data Mining Techniques ◽  
Network Output ◽  
Numerous Data ◽  
High Tariff ◽  
Entire Dataset

This paper explains the process of using neural networks, as one of numerous data mining techniques, for the classification of electricity consumers. The processed data comprised more than a million recordings of electricity consumption for 21,643 consumers over the period of four years and eight months. Using a data subset (70% of the entire dataset), the network was trained for the classification of consumers according to the type of the electric meter they possess (single-rate or dual-rate) and the zone they live in (city or village). The network input data in both cases included: consumer code, reading period from-to, current and previous meter reading for both low and high tariff, dual and single rate tariff consumption for that period and their total amount, as independent variables, whereas the network output comprised dependent variable classes (zone or type of electric meter). The results show that a network created in this way can be trained so well that it achieves high precision when evaluated using the test dataset. Using the available recordings about electricity consumption, the type of the electric meter consumers possess and the zone they live in can be predicted with the accuracy of 77% and 82%, respectively. These findings can provide the basis for further research using other data mining techniques.

Data mining based framework for exploring household electricity consumption patterns: A case study in China context

2018 ◽  
Vol 195 ◽  
pp. 773-785 ◽  
Author(s):  
Zhifeng Guo ◽  
Kaile Zhou ◽  
Xiaoling Zhang ◽  
Shanlin Yang ◽  
Zhen Shao
Keyword(s):  
Data Mining ◽  
Electricity Consumption ◽  
Consumption Patterns

Study of the operation of an artificial neural network that works in the electric network in order to prevent emergency conditions.

2020 ◽  
Vol 14 (1) ◽  
pp. 48-54
Author(s):  
D. Ostrenko ◽  
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy System ◽  
Electrical Networks ◽  
Electric Networks ◽  
Electric Network ◽  
Time Dynamics ◽  
Emergency Situations

Emergency modes in electrical networks, arising for various reasons, lead to a break in the transmission of electrical energy on the way from the generating facility to the consumer. In most cases, such time breaks are unacceptable (the degree depends on the class of the consumer). Therefore, an effective solution is to both deal with the consequences, use emergency input of the reserve, and prevent these emergency situations by predicting events in the electric network. After analyzing the source [1], it was concluded that there are several methods for performing the forecast of emergency situations in electric networks. It can be: technical analysis, operational data processing (or online analytical processing), nonlinear regression methods. However, it is neural networks that have received the greatest application for solving these tasks. In this paper, we analyze existing neural networks used to predict processes in electrical systems, analyze the learning algorithm, and propose a new method for using neural networks to predict in electrical networks. Prognostication in electrical engineering plays a key role in shaping the balance of electricity in the grid, influencing the choice of mode parameters and estimated electrical loads. The balance of generation of electricity is the basis of technological stability of the energy system, its violation affects the quality of electricity (there are frequency and voltage jumps in the network), which reduces the efficiency of the equipment. Also, the correct forecast allows to ensure the optimal load distribution between the objects of the grid. According to the experience of [2], different methods are usually used for forecasting electricity consumption and building customer profiles, usually based on the analysis of the time dynamics of electricity consumption and its factors, the identification of statistical relationships between features and the construction of models.

scholarly journals Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 588
Author(s):  
Felipe Leite Coelho da Silva ◽  
Kleyton da Costa ◽  
Paulo Canas Rodrigues ◽  
Rodrigo Salas ◽  
Javier Linkolk López-Gonzales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Approach ◽  
Electricity Consumption ◽  
Industrial Sector ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
The One ◽  
Mlp Model

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

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