Hybrid Short-term Load Research Articles

An intelligent hybrid short-term load forecasting model optimized by switching delayed PSO of micro-grids

Short-term load forecasting is an important part in the energy management of micro-grids, and the forecasting error directly affects the economic efficiency of operation. Considering the strong randomness and high volatility of loads on the user side of micro-grids, a short-term load forecasting method based on Empirical Mode Decomposition (EMD), Extreme Learning Machine with different Kernels, and Switching Delayed Particle Swarm Optimization (SDPSO) is proposed. First, the history load dataset is decomposed into several independent Intrinsic Mode Functions (IMFs) by EMD, and the sample entropy values of the IMFs are calculated. According to the approximation degree of sample entropy values, the IMFs are divided into three categories. Then, ELM with different kernels is adopted to forecast the three categories. Finally, the prediction results are summed to obtain the final prediction result. SDPSO is used to optimize the relevant parameters in the forecasting model. Three micro-grids with different capacities are used to verify the model, and the experimental results demonstrate that the proposed forecasting model can provide better accuracy than the other two methods. The proposed model can provide practical reference for the efficient operation of micro-grids.

An intelligent hybrid short-term load forecasting model for smart power grids

An accurate load forecasting is always particularly important for optimal planning and energy management in smart buildings and power systems. Millions of dollars can be saved annually by increasing a small degree of improvement in prediction accuracy. However, forecasting load demand accurately is a challenging task due to multiple factors such as meteorological and exogenous variables. This paper develops a novel load forecasting model, which is based on a feed-forward artificial neural network (ANN), to predict hourly load demand for various seasons of a year. In this model, a global best particle swarm optimization (GPSO) algorithm is applied as a new training technique to enhance the performance of ANN prediction. The fitness function is defined and a weight bias encoding/decoding scheme is presented to improve network training. Influential meteorological and exogenous variables along with correlated lagged load data are also empolyed as inputs in the presented model. The data of an ISO New England grid are used to validate the performance of the developed model. The results demonstrate that the proposed forecasting model can provide significanly better forecast accuracy, training performances and convergence characteristics than contemporary techniques found in the literature.

A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction

In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches.

A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network

Short-term load forecasting (STLF) plays a very important role in improving the economy and security of electricity system operations. In this paper, a hybrid STLF method is proposed based on the improved ensemble empirical mode decomposition (IEEMD) and back propagation neural network (BPNN). To alleviate the mode mixing and end-effect problems in traditional empirical mode decomposition (EMD), an IEEMD is presented based on the degree of wave similarity. By applying the IEEMD method, the nonlinear and nonstationary original load series is decomposed into a finite number of stationary intrinsic mode functions (IMFs) and a residual. Among these components, the high frequency (namely IMF1) is always so small that it has little contribution to model fitting, while it sometimes has a great disturbance for the STLF. Therefore, the IMF1 is removed in the proposed hybrid method for denoising. The remaining IMFs and residual are forecast by BPNN, and then the forecasting results of each component are combined with BPNN to obtain the final predicted load series. Three groups of studies were done to evaluate the effectiveness of the proposed hybrid method. The results show that the proposed hybrid method outperforms other methods both mentioned in this paper and previous studies in terms of all the three standard statistical indicators considered in this study.

A hybrid short-term load forecasting with a new input selection framework

This paper proposes a hybrid STLF (short-term load forecasting) framework with a new input selection method. BNN (Bayesian neural network) is used to forecast the load. A combination of the correlation analysis and ℓ2-norm selects the appropriate inputs to the individual BNNs. The correlation analysis calculates the correlation coefficients between the training inputs and output. The Euclidean distance with respect to a desired correlation coefficient is then calculated using the ℓ2-norm. The input sub-series with the minimum Euclidean norm is selected as the most correlated input and decomposed by a wavelet transform to provide the detailed load characteristics for BNN training. The sub-series whose Euclidean norms are closest to the minimum norm are further selected as the inputs for the individual BNNs. A weighted sum of the BNN outputs is used to forecast the load for a particular day. New England load data are used to evaluate the performance of the proposed input selection method. A comparison of the proposed STLF with the existing state-of-the-art forecasting techniques shows a significant improvement in the forecast accuracy.

A hybrid short-term load forecasting with a new data preprocessing framework

This paper proposes a hybrid load forecasting framework with a new data preprocessing algorithm to enhance the accuracy of prediction. Bayesian neural network (BNN) is used to predict the load. A discrete wavelet transform (DWT) decomposes the load components into proper levels of resolution determined by an entropy-based criterion. Time series and regression analysis are used to select the best set of inputs among the input candidates. A correlation analysis together with a neural network provides an estimation of the predictions for the forecasting outputs. A standardization procedure is proposed to take into account the correlation estimations of the outputs with their associated input series. The preprocessing algorithm uses the input selection, wavelet decomposition and the proposed standardization to provide the most appropriate inputs for BNNs. Genetic Algorithm (GA) is then used to optimize the weighting coefficients of different forecast components and minimize the forecast error. The performance and accuracy of the proposed short-term load forecasting (STLF) method is evaluated using New England load data. Our results show a significant improvement in the forecast accuracy when compared to the existing state-of-the-art forecasting techniques.

A Hybrid Short Term Load Forecasting Model of an Indian Grid

This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec-trical load forecasting considering the factors, past data of the load, respective weather condition and finan-cial growth of the people. These factors are derived by curve fitting technique. Then simulation has been conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a devel-oping country should be ignored as the development of a country is highly unpredictable. However, the im-portance of the past data should not be ignored. Here, just previous five years data are used to determine the above factors.

Implementation of hybrid short-term load forecasting system with analysis of temperature sensitivities

Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling, and for system security such as peak load shaving by power interchange with interconnected utilities. A novel hybrid load forecasting algorithm, which combines the fuzzy support vector regression method and the linear extrapolation based on similar days method with the analysis of temperature sensitivities is presented in this paper. The fuzzy support vector regression method is used to consider the lower load-demands in weekends and Monday than on other weekdays. The normal load in weekdays is forecasted by the linear extrapolation based on similar days method. Moreover, the temperature sensitivities are used to improve the accuracy of the load forecasting in relation to the daily load and temperature. The result demonstrated the accuracy of the proposed load forecasting scheme.

Parallel neural network-fuzzy expert system strategy for short-term load forecasting: system implementation and performance evaluation

The online implementation and results from a hybrid short-term electrical load forecaster that is being evaluated by a power utility are documented in this paper. This forecaster employs a new approach involving a parallel neural-fuzzy expert system, whereby Kohonen's self-organizing feature map with unsupervised learning, is used to classify daily load patterns. Post-processing of the neural network outputs is performed with a fuzzy expert system which successfully corrects the load deviations caused by the effects of weather and holiday activity. Being highly automated, little human interference is required during the process of load forecasting. A comparison made between this model and a regression-based model currently being used in the control centre has shown a marked improvement in load forecasting results.

Practical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting

The paper presents the development and practical implementation of a hybrid short-term electrical load forecasting model for a power system control centre. This hybrid architecture incorporates a Kohonen self-organising feature map with unsupervised learning for classification of daily load patterns, a supervised backpropagation neural network for mapping the temperature/load relationship, and a fuzzy expert system for postprocessing of neural network outputs. This load forecaster requires minimum operator intervention and can be trained adaptively on-line. The developed model has been tested extensively in the actual operating environment and has been shown to outperform the existing regression-based model.

Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems

In this paper, a hybrid model for short-term load forecast that integrates artificial neural networks and fuzzy expert systems is presented. The forecasted load is obtained by passing through two steps. In the first procedure, the artificial neural networks are trained with the load patterns corresponding to the forecasting hour, and the provisional forecasted load is obtained by the trained artificial neural networks. In the second procedure, the fuzzy expert systems modify the provisional forecasted load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. In the test case of 1994 for implementation in the short term load forecasting expert system of Korea Electric Power Corporation (KEPCO), the proposed hybrid model provided good forecasting accuracy of the mean absolute percentage errors below 1.3%. The comparison results with exponential smoothing method showed the efficiency and accuracy of the hybrid model. >