Computational Modeling of Electricity Consumption Using Econometric Variables Based on Neural Network Training Algorithms (original) (raw)

Forecast of electricity consumption based on an artificial neural network

PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL MANUFACTURING AND METALLURGY (ICIMM 2021), 2022

The study of the possibility of using an artificial neural network (ANN) to predict the hourly electricity consumption by the region of the Russian Federation was carried out. The data on electricity consumption and average daily temperature were selected as the initial data. The initial data were normalized so that they did not exceed the value of one. The number of training examples was 360 and each example had 3 inputs (the training dataset contained 1080 values). At the output, ANN calculated the hourly electricity consumption. A two-layer ANN was used for modeling. ANN was trained using the back propagation method and the conjugate gradient method. ANN training was carried out with an acceptable error of 0.00001 and was within 200 iterations of the conjugate gradient method. The use of an artificial neural network made it possible to satisfactorily describe the actual hourly electricity consumption. The average error was within 4%. The largest deviations of the actual and calculated values of hourly electricity consumption were observed around the 23rd, the 24th every day and amounted to 10-12%. The error increased as the forecast range increased. Calculations were carried out for a different number of neurons in the hidden layer: 7, 10, 13. These changes practically did not affect the forecast results. To increase the accuracy of the hourly forecast, it seems that the hourly temperature change during the day should be taken into account.

Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors

Energy Conversion and Management, 2008

This paper presents an artificial neural network (ANN) approach for annual electricity consumption in high energy consumption industrial sectors. Chemicals, basic metals and non-metal minerals industries are defined as high energy consuming industries. It is claimed that, due to high fluctuations of energy consumption in high energy consumption industries, conventional regression models do not forecast energy consumption correctly and precisely. Although ANNs have been typically used to forecast short term consumptions, this paper shows that it is a more precise approach to forecast annual consumption in such industries. Furthermore, the ANN approach based on a supervised multi-layer perceptron (MLP) is used to show it can estimate the annual consumption with less error. Actual data from high energy consuming (intensive) industries in Iran from 1979 to 2003 is used to illustrate the applicability of the ANN approach. This study shows the advantage of the ANN approach through analysis of variance (ANOVA). Furthermore, the ANN forecast is compared with actual data and the conventional regression model through ANOVA to show its superiority. This is the first study to present an algorithm based on the ANN and ANOVA for forecasting long term electricity consumption in high energy consuming industries.

Forecasting Electrical Energy Consumption using Artificial Neural Networks

International journal of engineering research and technology, 2019

Knowing how much the demandfor energy would be for the coming months would be very useful for electricity producing companies. Electricity can't be stored and energy companies face a challenge where the demand is always more and the supply is less. Hence the companies look for factors that affect electricity consumption and accurately forecast its usage. In this paper Neural Network methodology is proposed as an effective methodology for load forecasting

A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models

Mathematics

Production of electricity from the burning of fossil fuels has caused an increase in the emission of greenhouse gases. In the long run, greenhouse gases cause harm to the environment. To reduce these gases, it is important to accurately forecast electricity production, supply and consumption. Forecasting of electricity consumption is, in particular, useful for minimizing problems of overproduction and oversupply of electricity. This research study focuses on forecasting electricity consumption based on time series data using different artificial intelligence and metaheuristic methods. The aim of the study is to determine which model among the artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least squares support vector machines (LSSVMs) and fuzzy time series (FTS) produces the highest level of accuracy in forecasting electricity consumption. The variables considered in this research include the monthly electricity consumption over the years for differe...

Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption

arXiv (Cornell University), 2014

Due to imprecision and uncertainties in predicting real world problems, artificial neural network (ANN) techniques have become increasingly useful for modeling and optimization. This paper presents an artificial neural network approach for forecasting electric energy consumption. For effective planning and operation of power systems, optimal forecasting tools are needed for energy operators to maximize profit and also to provide maximum satisfaction to energy consumers. Monthly data for electric energy consumed in the Gaza strip was collected from year 1994 to 2013. Data was trained and the proposed model was validated using 2-Fold and K-Fold cross validation techniques. The model has been tested with actual energy consumption data and yields satisfactory performance.

Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks

Journal of Intelligent and Robotic Systems, 2001

This paper examines the application of artificial neural networks (ANNs) to the modelling and forecasting of electricity demand experienced by an electricity supplier. The data used in the application examples relates to the national electricity demand in the Republic of Ireland, generously supplied by the Electricity Supply Board (ESB). The paper focusses on three different time scales of interest to

Modelling Energy Demand Forecasting Using Neural Networks with Univariate Time Series

IEC2018 Proceedings Book, 2018

The new era of consumption and change in the behavior of people in developing countries that we facing in recent decades has made not only the energy sector but also all resource suppliers in different sectors not to fulfill the demand in the field. The electricity, which is one of the main power resources, has become one of the major issues to be overcome for the governments. Predicting the future energy demand is always the most valuable information to achieve any success in many sectors. In this paper, a daily forecasting of the maximum energy demand in Kurdistan region of Iraq is investigated based on an artificial natural network and sliding window techniques. The standard mean absolute percentage error method is used to evaluate the accuracy of forecasting models.

Forecasting net energy consumption using artificial neural network

Energy Sources, Part B, 2006

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Two different models were used in order to train the neural network: (i) Population, gross generation, installed capacity and years are used in input layer of network (Model 1). (ii) Energy sources are used in input layer of network (Model 2). The NEC is in output layer for two models. R 2 values for training data are equal to 0.99944 and 0.99913, for Model 1 and Model 2, respectively. Similarly, R 2 values for testing data are equal to 0.997386 and 0.999558 for Model 1 and Model 2, respectively. According to the results, the NEC prediction using ANN technique will be helpful in developing highly applicable and productive planning for energy policies.

Turkey's Forecasting of Energy Demand with Artificial Neural-Network

International Renewable Energy Conference (IRENEC 2017), 2017

Energy demand is increasing day by day in parallel with economic growth, especially for the rapidly developing countries. In order to achieve a sustainable economic growth, long-term targets are being put to manage the operation of market in a good way. Turkey is an emerging and rapidly developing country so its energy demand has increased rapidly to meet the growing economy. Therefore, forecasting Turkey's energy demand accurately is of great importance to achieve a sustainable economic growth. The main goal of this study is to develop the equation for forecasting energy demand using the backpropagation algorithm which is one of the artificial neural-network models to determine the future level of energy demand. This study presents the predictions for the years 2017-2020. The results of the energy demand estimations found in this study are compared with the official estimations of the MENR. It is concluded that official estimations for Turkey's energy demand are dramatically higher than forecasting value presented in this study.