Term Load Forecasting Research Articles

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

Pronósticos Demanda de Energía y Potencia a Corto Plazo. Caso de Estudio: Unidad de Control de Pronóstico Noroeste (Área Antioquia-Chocó)

El objetivo de este artículo es hacer un estudio del estado del arte referido a la Gestión de la Demanda de Energía y Potencia, caso particular, área operativa Antioquia – Choco, el marco regulatorio y las demás labores anexas como la elaboración semanal del pronóstico de demanda, sus estadísticas e indicadores y la gestión necesaria para asegurar la calidad. Finalmente se presentan propuestas de investigación que se pueden derivar sobre el asunto estudiado.

A non-linear ARMAX for short term load forecasting

This paper presents a methodology for forecasting the hourly load demand of the power provider enterprise using a non-linear autoregressive moving average model with exogenous variable (NARMAX). The NARMAX method can use fewer order terms and more combination terms to capture the dynamic characteristics of highly non-linear system with orthogonal parameter estimation. And the NARMAX method is expected to be improved the model identification and fewer orders in the short term load forecasting. In this paper, the proposed NARMAX method is used to test on a practice power provider enterprise (Taiwan power supply networks) for short term load forecasting to illustrate its performance.

Electricity Load Forecasting based on Framelet Neural Network Technique

Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This study shows Electricity Load Forecasting modeling based on Framelet Neural Network Technique (FNN) for Baghdad City. Framelet technique is implemented to the time series data, decomposing the data into number of Framelet coefficient signals. The decomposed signals are then fed into neural network for training. To obtain the predict forecast, the outputs from the neural network are recombined using the same Framelet technique. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in short term load forecast.

Short Term Load Forecasting for Erbil Distribution System Using ANN and Wavelet Transform

In this paper an approach is proposed for Short Term Load Forecasting (STLF) which combines Wavelet Transform (WT) and Artificial Neural Network (ANN). It is well known that the electrical load at any time can be considered as a linear combination of different frequencies. The daily load curves are decomposed into approximation part associated with low frequency and some detail parts associated with high frequency by means of (WT). Feed Forward Neural Networks are trained by low frequencies and corresponding average temperature or maximum and minimum temperature to predict the approximation part for the next seven days. The short term load is forecasted by summing the predicted approximation part and the mean of the detail parts of the last three same type days. This approach had been tested by load of Erbil, the capital of Iraq Kurdistan Region. The result have showed an encouraging accuracy, given that the available data is inaccurate.

Input space to neural network based load forecasters

The literature has presented a great variety of methodologies for electricity load forecasting. This fact reflects the possible consequences of electricity load forecasting errors, which can cause blackouts with serious social and economic implications. In recent years, papers on this topic have shown the potential of neural network based models. For short term horizons in particular, this capability is explained by the neural networks' flexibility in capturing nonlinear interdependencies between the load and exogenous variables. However, one issue in the development of neural network based electricity load forecasts has not been treated with proper care. Input space representation and complexity control interact strongly, and new solutions for this problem are needed. This paper proposes original automatic procedures for selecting input variables in feedforward neural network based load forecasting models. Filter (model independent) and wrapper (model dependent) approaches have both been investigated in the context of short term load forecasting. Regarding the filter approach, a procedure for identifying relevant subspaces is introduced. For each relevant subspace, interdependences based on a normalized measure of mutual information are estimated to select input variables. Furthermore, phase-space embedding, based on Takens' theorem, is applied in combination with multi-resolution decomposition via wavelet transformation. The results have been compared with a Bayesian wrapper, in which probes have been employed as input references of usefulness.

Bayesian neural network approach to short time load forecasting

Short term load forecasting (STLF) is an essential tool for efficient power system planning and operation. We propose in this paper the use of Bayesian techniques in order to design an optimal neural network based model for electric load forecasting. The Bayesian approach to modelling offers significant advantages over classical neural network (NN) learning methods. Among others, one can cite the automatic tuning of regularization coefficients, the selection of the most important input variables, the derivation of an uncertainty interval on the model output and the possibility to perform a comparison of different models and, therefore, select the optimal model. The proposed approach is applied to real load data.

BP neural network with rough set for short term load forecasting

Precise Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors to develop the accuracy of predictions. Through attribute reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical power system, we tested the performance of RSBP by comparing its predictions with that of BP network.

A new support vector machine optimized by improved particle swarm optimization and its application

A new support vector machine (SVM) optimized by an improved particle swarm optimization (PSO) combined with simulated annealing algorithm (SA) was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization (SAPSO) was enhanced, and the searching capacity of the particle swarm optimization was studied. Then, the improved particle swarm optimization algorithm was used to optimize the parameters of SVM (c, σ and e). Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.

Improved wavelet neural network combined with particle swarm optimization algorithm and its application

An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.

Fuzzy approach for short term load forecasting

The main objective of short term load forecasting (STLF) is to provide load predictions for generation scheduling, economic load dispatch and security assessment at any time. The STLF is needed to supply necessary information for the system management of day-to-day operations and unit commitment. In this paper, the ‘time’ and ‘temperature’ of the day are taken as inputs for the fuzzy logic controller and the ‘forecasted load’ is the output. The input variable ‘time’ has been divided into eight triangular membership functions. The membership functions are Mid Night, Dawn, Morning, Fore Noon, After Noon, Evening, Dusk and Night. Another input variable ‘temperature’ has been divided into four triangular membership functions. They are Below Normal, Normal, Above Normal and High. The ‘forecasted load’ as output has been divided into eight triangular membership functions. They are Very Low, Low, Sub Normal, Moderate Normal, Normal, Above Normal, High and Very High. Case studies have been carried out for the Neyveli Thermal Power Station Unit-II (NTPS-II) in India. The fuzzy forecasted load values are compared with the conventional forecasted values. The forecasted load closely matches the actual one within ±3%.

Short term and medium term power distribution load forecasting by neural networks

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey.

Collaboration as a gateway to nursing research in the ED

This paper presents an intelligent mechanism for Short Term Load Forecasting (STLF) models, which allows self-adaptation with respect to the load operational conditions. Specifically, a knowledge-based FeedBack Tunning Fuzzy System (FBTFS) is proposed to instantaneously correlate the information about the demand profile and its operational conditions to make decisions for controlling the model’s forecasting error rate. To maintain minimum forecasting error under various operational scenarios, the FBTFS adaptation was optimised using a Multi-Layer Perceptron Artificial Neural Network (MLPANN), which was trained using Backpropagation algorithm, based on the information about the amount of error and the operational conditions at time of forecasting. For the sake of comparison and performance testing, this mechanism was added to the conventional forecasting methods, i.e. Nonlinear AutoRegressive eXogenous-Artificial Neural Network (NARXANN), Fuzzy Subtractive Clustering Method-based Adaptive Neuro Fuzzy Inference System (FSCMANFIS) and Gaussian-kernel Support Vector Machine (GSVM), and the measured forecasting error reduction average in a 12 month simulation period was 7.83%, 8.5% and 8.32% respectively. The 3.5 MW variable load profile of Edith Cowan University (ECU) in Joondalup, Australia, was used in the modelling and simulations of this model, and the data was provided by Western Power, the transmission and distribution company of the state of Western Australia.

Forecasting daily urban electric load profiles using artificial neural networks

The paper illustrates a combined approach based on unsupervised and supervised neural networks for the electric energy demand forecasting of a suburban area with a prediction time of 24 h. A preventive classification of the historical load data is performed during the unsupervised stage by means of a Kohonen's self organizing map (SOM). The actual forecast is obtained using a two layered feed forward neural network, trained with the back propagation with momentum learning algorithm. In order to investigate the influence of climate variability on the electricity consumption, the neural network is trained using weather data (temperature, relative humidity, global solar radiation) along with historical load data available for a part of the electric grid of the town of Palermo (Italy) from 2001 to 2003. The model validation is performed by comparing model predictions with load data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short term load forecasting (STLF) problem also at so small a spatial scale as the suburban one.

Self Organization and Learning Methods in Short Term Electric Load Forecasting: A Review

This paper reviews unconventional methodsused in short-term loadforecasting. The basic theory of these methods and their suitability to short term load forecasting is discussed. Application and the basic formulation strategy adopted for the purpose are also discussed. These methods are classified into supervised and unsupervised learning and self-organizing with optimization categories. Different models of artificial neural network, fuzzy logic, evolutionary programming, simulatedannealing, learningmachine, andexpert system have been dealt within appropriate classification of each.

INPUT VARIABLE SELECTION FOR FORECASTING MODELS

The selection of input variables plays a crucial role when modelling time series. For nonlinear models there are not well developed techniques such as AIC and other criteria that work with linear models. In the case of Short Term Load Forecasting (STLF) generalization is greatly influenced by such selection. In this paper two approaches are compared using real data from a Spanish utility company. The models used are neural networks although the algorithms can be used with other nonlinear models. The experiments show that that input variable selection affects the performance of forecasting models and thus should be treated as a generalization problem.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Short-term hourly load forecasting using time-series modeling with peak load estimation capability

This paper presents a new time series modeling for short term load forecasting, which can model the valuable experiences of the expert operators. This approach can accurately forecast the hourly loads of weekdays, as well as, of weekends and public holidays. It is shown that the proposed method can provide more accurate results than the conventional techniques, such as artificial neural networks or Box-Jenkins models. In addition to hourly loads, daily peak load is an important problem for dispatching centers of a power network. Most of the common load forecasting approaches do not consider this problem. It is shown that the proposed method can exactly forecast the daily peak load of a power system. Obtained results from extensive testing on the Iran's power system network confirm the validity of the developed approach.

Wavelet transform and neural networks for short-term electrical load forecasting

Demand forecasting is key to the efficient management of electrical energy systems. A novel approach is proposed in this paper for short term electrical load forecasting by combining the wavelet transform and neural networks. The electrical load at any particular time is usually assumed to be a linear combination of different components. From the signal analysis point of view, load can also be considered as a linear combination of different frequencies. Every component of load can be represented by one or several frequencies. The process of the proposed approach first decomposes the historical load into an approximate part associated with low frequencies and several detail parts associated with high frequencies through the wavelet transform. Then, a radial basis function neural network, trained by low frequencies and the corresponding temperature records is used to predict the approximate part of the future load. Finally, the short term load is forecasted by summing the predicted approximate part and the weighted detail parts. The approach has been tested by the 1997 data of a practical system. The results show the application of the wavelet transform in short term load forecasting is encouraging.