Application of artificial neural networks to historical data analysis for short-term electric load forecasting (original) (raw)

Short Term Load Forecasting in Electric Power Systems with Artificial Neural Networks

The demand in electric power should be predicted with the highest possible accuracy as it affects decisively many of power system's operations. Conventional methods for load forecasting were built on several assumptions, while they had to cope with relations between the data used that could not be described analytically. Artificial Neural Networks (ANNs) gave answers to many of the above problems and they became the predominant load forecasting technique. In this chapter the reader is first introduced to Artificial Neural Networks and their usage in forecasting the load demand of electric power systems. Several of the major training techniques are described with their pros and cons being discussed. Finally, feed-forward ANNs are used for the short-term forecasting of the Greek Power System load demand. Various ANNs with different inputs, outputs, numbers of hidden neurons etc. are examined, techniques for their optimization are proposed and the obtained results are discussed.

Electric load forecasting using an artificial neural network

Power Systems, …, 1991

This paper presents an artificial neural network(ANN) approach t o electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead forecasts in our test on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24hour ahead forecasts with a currently used forecasting technique applied to the same data.

Short-Term Power System Hourly Load Forecasting Using Artificial Neural Networks

2017

Artificial neural networks (ANN) have been used for many application in various sectors. The learning property of an ANN algorithm in solving both linear and non-linear problems can be utilized and applied to different forecasting problems. In the power system operation load forecasting plays a key role in the process of operation and planning. This paper present the development of an ANN based short-term hourly load forecasting model applied to a real data from MIBEL – Iberian power market test case. The historical data for 2012 and 2013 ware used for a Multilayer Feed Forward ANN trained by Levenberg-Marquardt algorithm. The forecasted next day 24 hourly peak loads and hourly consumptions are generated based on the stationary output of the ANN with a performance measured by Mean Squared Error (MSE) and MAPE (Mean Absolute Percentage Error). The results have shown good alignment with the actual power system data and have shown proposed method is robust in forecasting future (short-...

An efficient approach for short term load forecasting using artificial neural networks

International Journal of …, 2006

In previous work, we applied artificial neural networks (ANN) for short term load forecasting using real load and weather data from the Hydro-Quebec databases where three types of variables were used as inputs to the neural network: (a) hour and day indicators, (b) weather related inputs and (c) historical loads. In general, for forecasting with a lead time of up to a few days ahead, load history (for the last few days) is not available, and therefore, estimated values of this load are used instead. However, a small error in these estimated values may grow up dramatically and lead to a serious problem in load forecasting since this error is fed back as an input to the forecasting procedure. In this paper, we demonstrate ANN capabilities in load forecasting without the use of load history as an input. In addition, only temperature (from weather variables) is used, in this application, where results show that other variables like sky condition (cloud cover) and wind velocity have no serious effect and may not be considered in the load forecasting procedure.

Neural network based approach for short-term load forecasting

2009 IEEE/PES Power Systems Conference and Exposition, 2009

Short-term load forecast is an essential part of electric power system planning and operation. Forecasted values of system load affect the decisions made for unit commitment and security assessment, which have a direct impact on operational costs and system security. Conventional regression methods are used by most power companies for load forecasting. However, due to the nonlinear relationship between load and factors affecting it, conventional methods are not sufficient enough to provide accurate load forecast or to consider the seasonal variations of load. Conventional ANN-based load forecasting methods deal with 24-hour-ahead load forecasting by using forecasted temperature, which can lead to high forecasting errors in case of rapid temperature changes. This paper presents a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network. Correlation analysis of weather data determines the input parameters of the neural networks. The suitability of the proposed approach is illustrated through an application to the actual load data of the Egyptian Unified System.

Short-term load forecasting using an artificial neural network

Power Systems, IEEE Transactions …, 1992

Artificial Neural Network (ANN) Method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern include Saturday, Sunday, and Monday loads. A nonlinear load model is proposed and several structures of ANN for short-term load forecasting are tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers are tested with various combination of neurons, and results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives good load forecast.

Short term electric load forecast using artificial neural networks

Proceedings of MELECON '94. Mediterranean Electrotechnical Conference, 1994

Short Term Load Forecasting Using Artificial Neural Networks

Artificial neural network (ANN) has been used for many years in sectors and disciplines like medical science, defence industry, robotics, electronics, economy, forecasts, etc. The learning property of ANN in solving nonlinear and complex problems called for its application to forecasting problems. This report present the development of an ANN based short-term load forecasting model for the 132/33KV sub- Station, Kano, Nigeria. The recorded daily load profile with a lead time of 1-24 hours for the year 2005 was obtained from the utility company. The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB® R2008b ANN Toolbox. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training parameters are described. The forecasted next day 24 hourly peak loads are obtained based on the stationary output of the ANN with a performance Mean Squared Error (MSE) of 􀫞. 􀫡􀫝􀢋􀬿􀫟 and compares favorably with the actual Power utility data. The results have shown that the proposed technique is robust in forecasting future load demands for the daily operational planning of power system distribution sub-stations in Nigeria.

Short Term Load Forecasting Using Artificial Neural Networks for the West of Iran

Journal of Applied Sciences, 2007

Short term Electric load forecasting is an important aspect of power system planning and operation for utility companies. Short term load forecasting (STLF) has always been one of the most critical, sensitive and accuracy demanding factors of the power systems. An accurate STLF improves not only the systems economic viability but also its safety, stability and reliability. The researcher presented in this works support Artificial Neural Network and Time Series Methods techniques in short term forecasting. This paper presents an investigation for the short term (one day to seven days, & every months of one year) load forecasting the load demand of Nepal Electricity Authority (NEA) in Bishnumati Feeder of Balaju Substation, by using artificial neural network and time series methods.

ARTIFICIAL NEURAL NETWORKS APPROACH BASED SHORT TERM ELECTRIC LOAD FORECASTING

IJCIRAS, 2020

The term load prediction refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that vital power system expansion decisions can be made. In recent years, there is an emphasis on Short-Term Load Forecasting (STLF), the essential part of power system planning and operation. Rudimentary operating functions such as unit commitment, economic transmit, and unit preservation can be performed efficiently with a precise forecast. Short-term forecasting can assist in predicting the flow and making decisions that prevent overloading. This paper implements the STLF as a 24-hour forecast whose result is an hourly electric forecast. This paper uses the method of Artificial Neural Network (ANN) to create a STLF process. The inputs to the ANN are load profiles of one month previous days and the weather variables of that days. Correlation analysis between load and weather variables will be used for all predictor input data to the ANN to optimize in size and accuracy. MATLAB programming language is used to implement this system.

Application of artificial neural networks to historical data analysis for short-term electric load forecasting (original) (raw)

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