Integrated vs. total approach in short-term load forecasting (original) (raw)

Short-term load forecasting in large scale electrical utility using artificial neural network

2013

This paper presents a novel method for short-term load forecasting (STLF), based on artificial neural network (ANN), targeted for use in large-scale systems such as distribution management system (DMS). The system comprises of a preprocessing unit (PPU) and a feed forward ANN ordered in a sequence. PPU prepares the data and feeds them as input to the ANN, which calculates the hourly load forecasts. Preprocessing of the entering data reduces the size of the input space to the ANN, which improves the generalization capability and shortens the training time of the network. Reduced dimension of the input space also diminishes the number of parameters to be set in a training procedure, allowing smaller training set, and thus online usage and adaptation. This is important for a real-world power system where a sufficient set of historical data (training points) may not always be available, for different reasons. Ease of use and fast adaptation are necessary when predictions need to carry o...

Applying Machine Learning Techniques to Short Term Load Forecasting

2015

This thesis reports on the application of two machine learning techniques on the case of 24-ahead short term load forecasting (STLF). The methods used are Random Forests and Echo State Networks. Hierarchical linear models are used as baseline comparison. Four different cases of STLF will be combined in this research: Total power consumption of an area, power demand on the power supplier, power supply to the power network, and solar power generation (SPG). These variables are useful things to know in power supply planning by power suppliers and short term peak detection for network operators. To know these variables beforehand means to be able to economically and securely operate the power grid and power supply. Therefore constant research is being done to improve forecasting techniques. More recently it has become important to incorporate the supply by users into the forecasting system as more and more households install solar panels. A dataset was used from a neighbourhood in The N...

PROMOTING SHORT TERM LOAD FORECASTING BY USING ARTIFICIAL INTELLIGENCE

Load forecasting has always been the essential part of an efficient power system planning and operation. Several electric power companies are now forecasting power demand (load) based on conventional methods and some on the behalf of artificial intelligence. On the behalf of that review, this paper presents the short term load forecasting on the basis of MAPE and MAE accuracy criteria by using time series as an input pattern selection for neural networks. The input data is trained by FFNN and in an another model to overcome the drawback of Gradient descent algorithm problem of local minima another global search algorithms GA is used for initialization of input parameters of neural network called GANN technique. The data used for the analysis is collected for the year 2012 from Ontario Electricity Market and prediction is done on time series frame work for first four weeks of December 2012.

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.

Load Forecasting Using Neural Network Integrated with Economic Dispatch Problem

2018

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the opti...

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.

Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview

Energies, 2019

Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed.