Short-term Load Forecasting Model Research Articles

Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter

Accurate load forecasting is an important issue for the reliable and efficient operation of the power system. This paper presents a hybrid algorithm which combines SVR (support vector regression), RBFNN (radial basis function neural network), and DEKF (dual extended Kalamn filter) to construct a prediction model (SVR–DEKF–RBFNN) for short-term load forecasting. In the proposed model, first, the SVR model is employed to determine both the structure and initial parameters of the RBFNN. After initialization, the DEKF is used as the learning algorithm to optimize the parameters of the RBFNN. Finally, the optimal RBFNN model is adopted to predict short-term load. The performance of the proposed approach is evaluated on real-load data from the Taipower Company, and compared with DEKF–RBFNN and GRD-RBFNN (gradient decent RBFNN) models. Simulation results of three cases show that the proposed method has better forecasting performance than the other methods.

Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting

The electric grid is changing. With the smart grid the demand response (DR) programs will hopefully make the grid more resilient and cost efficient. However, a scheme where consumers can directly participate in demand management requires new efforts for forecasting the electric loads of individual consumers. In this paper we try to find answers to two main questions for forecasting loads for individual consumers: First, can current short term load forecasting (STLF) models work efficiently for forecasting individual households? Second, do the anthropologic and structural variables enhance the forecasting accuracy of individual consumer loads? Our analysis show that a single multi-dimensional model forecasting for all houses using anthropologic and structural data variables is more efficient than a forecast based on traditional global measures. We have provided an extensive empirical evidence to support our claims.

Weighted Time-Variant Slide Fuzzy Time-Series Models for Short-Term Load Forecasting

Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors such as big sport events or popular TV shows can change demand consumption in particular hours, which will lead to sudden load changes. A weighted time-variant slide fuzzy time-series model (WTVS) for short-term load forecasting is proposed to improve forecasting accuracy. The WTVS model is divided into three parts, including the data preprocessing, the trend training and the load forecasting. In the data preprocessing phase, the impact of random factors will be weakened by smoothing the historical data. In the trend training and load forecasting phase, the seasonal factor and the weighted historical data are introduced into the Time-variant Slide Fuzzy Time-series Models (TVS) for short-term load forecasting. The WTVS model is tested on the load of the National Electric Power Company in Jordan. Results show that the proposed WTVS model achieves a significant improvement in load forecasting accuracy as compared to TVS models.

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.

Forecasting of Short-Term Electric Load Using Application of Wavelets with Feed-Forward Neural Networks

An accurate and efficient Short-Term Load Forecasting (STLF) plays a vital role for economic operational planning of both regulated and restructured power systems. This paper presents the STLF models by introducing wavelet transforms, in different ways, with feed-forward Neural Networks (NNs). First, a wavelet-based NN is modeled, where the forecasting has been accomplished in three-stages and the wavelet technique is employed to decompose/reconstruct the original signals and non-linearity of the decomposed signals. Second, an Adaptive Wavelet Neural Network (AWNN) is modeled, which is a new class of NN with continuous wavelet function as the hidden layer node's activation function. Unlike the first model, AWNN does not externally decompose/ reconstruct the original signals and, therefore, this model deals with the problem related to loss of high frequency information that might occur in the wavelet-based NN model. AWNN continuously updates the wavelet parameters (translation and dilation) and layer weights through a back-propagation training algorithm as in classical NNs. The performances of these two models are compared with Multi-Layer Perceptron NN (MLPNN) with the application of day-ahead and hour-ahead load forecasting in the California electricity market. The results are also compared with California Independent System Operator (CAISO)'s forecasted system loads. It is found that due to faster and accurate training capability, AWNN outperforms the MLPNN, wavelet-based NN and CAISO load forecasts.

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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.

04/01894 Fuzzy short-term electric load forecasting: Al-Kandari, A. M. et al. International Journal of Electrical Power & Energy Systems, 2004, 26, (2), 111–122

An artificial neural network (ANN) model for short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting the next 24-hour load profile at one time, as opposed to the usual ‘next one hour’ ANN models. The inputs to the ANN are load profiles of the two previous days and daily maximum and minimum temperature forecasts. The network is trained to learn the next day's load profile. Testing of the model with one year of data from the Greek interconnected power system resulted in a 2.66% average absolute forecast error.

Novel approach considering load-relative factors in short-term load forecasting

With structural changes to electricity industry in recent years, there is an increasing emphasis on short-term load forecasting (STLF). STLF is the essential part of power system planning and operation. In deregulated environment, STLF greatly affects system marginal price. Therefore many kinds of models for STLF have been proposed in recent 30 years. However, an individual forecasting model cannot work well in every possible condition. To overcome this problem, an integrated model, based on the idea of simulative forecasting, which can synthesize individual forecasting model results, is proposed in this paper. In practice, STLF is always affected by a variety of nonlinear factors. Since it is difficult to identify the nonlinearity by conventional methods, a novel approach is proposed which is able to deal with most factors affecting power load. The major idea in the approach is to define a mapping function for each factor. Finally, an algorithm for optimizing mapping functions is presented. Case studies show that it is a successful solution to improve the accuracy of STLF.

A load curve based fuzzy modeling technique for short-term load forecasting

A modeling method is suggested in this paper that permits building fuzzy models for short-term load forecasting (STLF). The model building process is divided in three parts: (a) the structure identification based on a fuzzy C-regression method, (b) selection of the proper model inputs which is achieved using a genetic algorithm based selection mechanism, and (c) fine tuning by means of a hybrid genetic/least squares algorithm. To obtain simple and efficient models we employ two descriptions for the load curves ( LC's), namely, the feature description for the premise part and the cubic B-spline curve for the consequent part of the rules. The simulation results demonstrate that the suggested model exhibits very good forecast capabilities. The suggested model is favorably compared with the ANN model in terms of prediction accuracy, robustness and model complexity.

A novel approach to short-term load forecasting using fuzzy neural networks

An efficient modeling technique based on the fuzzy curve notion is developed in this paper to generate fuzzy models for short-term load forecasting. The suggested forecasting approach proceeds on the following steps: (a) prediction of the load curve extremals (peak and valley loads) using separate fuzzy models; (b) formulation of the representative day based on historical load data; and (c) mapping of the representative day load curve to the forecasted peak values to obtain the predicted day load curves. Very good prediction performance is attained as shown in the simulation results which verify the effectiveness of the modeling technique.

A novel neuro-fuzzy based self-correcting online electric load forecasting model

The paper presents a neuro-fuzzy short-term load forecasting (STLF) model. The proposed ANN function approximator models the relationships between the system hourly peak load and system variables affecting it, namely, weather and temperature variations, type and time of day, the inherent parameters of historical load patterns such as trend, cyclic oscillations, regular seasonal and irregular ‘special’ events. The load predictor forecasting input vector was extended to account for most of the input dominant variables affecting the short-term forecast load. The model utilizes a preprocessor for input vector generation and priority classifications using historical load and system data. A postprocessor fuzzy logic block provides error correction and data filtering and online tuning and adjustment of electric load forecast data.

Short-term load forecasting using neural networks

Abstract An artificial neural network (ANN) model for short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting the next 24-hour load profile at one time, as opposed to the usual ‘next one hour’ ANN models. The inputs to the ANN are load profiles of the two previous days and daily maximum and minimum temperature forecasts. The network is trained to learn the next day's load profile. Testing of the model with one year of data from the Greek interconnected power system resulted in a 2.66% average absolute forecast error.

The application of short-term adaptive forecasting techniques in energy management for the control of electrical load

The philosophy of Energy Management has been developed in order to improve operational and economic efficiencies in the supply of electrical energy at the times of peak demands. These peaks last for only a short time and are costly for the supply authority. Energy Management attempts to shift the use of electrical energy from the times of peak demand to those times where the level of the load is relatively low. There are two distinct divisions within Energy Management which fulfil this requirement: indirect and direct load control. First, through the pricing of electricity, large industrial and commercial consumers can react indirectly in response to pre-published tariffs or to spot prices. Second, the supply authority could employ direct control of suitable domestic appliances. This would require a communications facility between the supplier and the consumer terminal equipment which could be remotely controlled. This paper analyses and simulates the effects of the introduction of a direct load-control strategy. The load to be controlled would be domestic appliances which are modelled by employing a stochastic approach. This would identify the available level of controllable load at any time of the day. The principal effects of Energy Management are the improvement of system load factors and the reduction of peak loads, thus improving the supply economies and efficiencies. For the consumer, Energy Management could reduce the energy costs for domestic customers with minimal inconvenience, provided that the control strategy is chosen with care. The paper presents an adaptive-time-series model for short-term load forecasting and incorporates the appliance models in order to estimate the expected response of the domestic load to transmitted control signals. Simulations of simple control strategies are presented showing the avoidance of potentially difficult conditions of simultaneous reconnection of the controlled load.

Short-term load forecasting

This paper discusses the state of the art in short-term load forecasting (STLF), that is, the prediction of the system load over an interval ranging from one hour to one week. The paper reviews the important role of STLF in the on-line scheduling and security functions of an energy management system (EMS). It then discusses the nature of the load and the different factors influencing its behavior. A detailed classification of the types of load modeling and forecasting techniques is presented. Whenever appropriate, the classification is accompanied by recommendations and by references to the literature which support or expand the discussion. The paper also presents a lengthy discussion of practical aspects for the development and usage of STLF models and packages. The annotated bibliography offers a representative selection of the principal publications in the STLF area.