Forecasting electrical consumption by integration of Neural Network, time series and ANOVA (original) (raw)
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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.
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.
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 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
Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models
International Journal of Business and Management, 2016
In many areas such as financial, energy, economics, the historical data are non-stationary and contain trend and seasonal variations. The goal is to forecast the energy consumption in U.S. using two approaches, namely the statistical approach (SARIMA) and Neural Networks approach (ANN), and compare them in order to find the best model for forecasting. The energy area has an important role in the development of countries, thus, consumption planning of energy must be made accurately, despite they are governed by other factors such that population, gross domestic product (GDP), weather vagaries, storage capacity etc. This paper examines the forecasting performance for the residential energy consumption data of United States between SARIMA and ANN methodologies. The multi-layer perceptron (MLP) architecture is used in the artificial neural networks methodology. According to the obtained results, we conclude that the neural network model has slight superiority over SARIMA model and those models are not directional.
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.
Neural Network World, 2017
Recently, there has been a significant emphasis in the forecasting of the electricity demand due to the increase in the power consumption. Energy demand forecasting is a very important task in the electric power distribution system to enable appropriate planning for future power generation. Quantitative and qualitative methods have been utilized previously for the electricity demand forecasting. Due to the limitations in the availability of data, these methods fail to provide effective results. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. This paper presents the computational modeling of electricity consumption based on the Neural Network (NN) training algorithms. The main aim of the work is to determine the optimal training algorithm for electricity demand forecasting. From the experimental analysis, it is concluded that the Bayesian regularization training algorithm exhibits low relative error and high correlation coefficient than other training algorithms. Thus, the Bayesian Regularization training algorithm is selected as the optimal training algorithm for the effective prediction of the electricity demand. Finally, the economic input attributes are forecasted for next 15 years using time series forecasting. Using this forecasted economic attributes and with the optimal Bayesian Regularization training algorithm, the electricity demand for the next 15 years is predicted. The comparative analysis of the NN training algorithms for the proposed dataset and larger datasets obtained from the UCI repository and American Statistical Association shows that the Bayesian Regularization training algorithm yields higher correlation value and lower relative error than other training algorithms.
Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network
Neural Computing and Applications, 2017
Electricity is one of the most important end-user energy types in today's world and has an effective role in development of societies and economies. Stability of electricity supply is provided by matching of generated and consumed electricity amount during the all-day. So, electricity consumption forecasting is an essential issue for electric utilities. In this study, the monthly electricity demand of Turkey has been predicted. To model the effects of seasonality and trend, four different ANN models have been developed and selected the superior one. In addition, the selected ANN model has been compared with SARIMA model in order to increase the acceptability and reliability of the ANN model. The monthly electricity demand of Turkey has been predicted between 2015 and 2018 via the ANN model that can make successful and high-accuracy predictions according to the performance measures. The forecasting values will help in determining the mediumterm and stable energy policies.