Model For Short-term Load Research Articles

A decomposition-based approximate entropy cooperation long short-term memory ensemble model for short-term load forecasting

A decomposition-based approximate entropy cooperation long short-term memory ensemble model for short-term load forecasting

Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

Comparative Analysis of Intelligently Tuned Support Vector Regression Models for Short Term Load Forecasting in Smart Grid Framework

A large amount of work has been taken place, if we talk about forecasting in the fields of power system. Various reforms in the existing techniques have proved to be helpful in providing guidance to researchers for developing efficient algorithms exhibiting greater accuracy. This paper presents three forecasting models viz. three-day-trained Support Vector Regression model and parameter optimized Support Vector Regression using Genetic Algorithm (SVRGA) and that using Particle Swarm Optimization (SVRPSO). Unlike existing models, these models accomplish accurate forecasting by optimizing the regularized structural risk function. The models make use of previous three days hourly load data for predicting next day hourly load. This paper performs a comparative study between GA and PSO on the grounds of optimization of the hyper-parameters of SVR model.

A Hybrid Model for Short-Term Load Forecasting Based on Non-Parametric Error Correction

A Hybrid Model for Short-Term Load Forecasting Based on Non-Parametric Error Correction

A refined parametric model for short term load forecasting

We present a refined parametric model for forecasting electricity demand which performed particularly well in the recent Global Energy Forecasting Competition (GEFCom 2012). We begin by motivating and presenting a simple parametric model, treating the electricity demand as a function of the temperature and day of the data. We then set out a series of refinements of the model, explaining the rationale for each, and using the competition scores to demonstrate that each successive refinement step increases the accuracy of the model’s predictions. These refinements include combining models from multiple weather stations, removing outliers from the historical data, and special treatments of public holidays.

A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting

In the new competitive electricity markets, the necessity of appropriate load forecasting tools for accurate scheduling is completely evident. The model which is utilised for the forecasting purposes determines how much the forecasted results would be dependable. In this regard, this paper proposes a new hybrid forecasting method based on the wavelet transform, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) for short-term load forecasting. In the proposed model, the autocorrelation function and the partial autocorrelation function are utilised to see the stationary or non-stationary behaviour of the load time series. Then, by the use of Akaike information criterion, the appropriate order of the ARIMA model is found. Now, the ARIMA model would capture the linear component of the load time series and the residuals would contain only the nonlinear components. The nonlinear part would be decomposed by the discrete wavelet transform into its sub-frequencies. Several ANNs are applied to the details and approximation components of the residuals signal to predict the future load sample. Finally, the outputs of the ARIMA and ANNs are summed. The empirical results show that the proposed hybrid method can improve the load forecasting accuracy suitably.

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.

Fuzzy logic for short term load forecasting

The results of an investigation of a fuzzy logic model for short term load forecasting are presented. The proposed methodology uses fuzzy rules to incorporate historical weather and load data. These fuzzy rules are obtained from the historical data using a learning-type algorithm. Test results from daily peak and total load forecasts for one year of data from a large scale power system indicate that the fuzzy rule bases can produce results similar in accuracy to more complicated statistical and back propagation neural network methods.

Load models for short-term load forecasting in multinode power systems

The problem of on-line short-term load demand forecasting at all the major loading nodes of an interconnected power system is studied. A multivariable state space model is proposed. The innovations representation is utilized, so that an ordinary recursive least-squares algorithm can be used to give unbiased estimates of the model parameters. State estimation is carried out by using adaptive estimation techniques. The parameter identification and state estimation algorithms are combined in a bootstrap manner. A scheme for detecting abnormal load behaviour is proposed. A weather sensitive load model is also presented for longer load time forecasts. Actual data provided by the Ontario Hydro for four loading buses are used for five-minute, ten-minute and hourly forecasts. The results show that the proposed approach is very attractive.

An Accurate Model for Short-Term Load Forecasting

An Accurate Model for Short-Term Load Forecasting

An Accurate Model for Short-Term Load Forecasting

This paper presents a simple algorithm for precise short-term load forecasting. This forecast is essentially for monitoring and controlling power system operation. An adaptive model based on records of the hourly loads and weather informations is introduced. Detection for the bad data is made. The daily and weekly correlations are considered. An adaptive error estimation ( mean and variance ) is used to have the expected loads on a probabilistic base. The proposed model has the advantage of low storage requirements and quick calculations easily implemented in on-line computer system. Application of the proposed algorithm on real data banks is made and a reasonable accuracy is obtained.