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Load Forecasting (load + forecasting)

Distribution by Scientific Domains
Engineering75%

Kinds of Load Forecasting

  • short-term load forecasting
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    Selected Abstracts

    Short-term load forecasting using informative vector machine

    ELECTRICAL ENGINEERING IN JAPAN, Issue 2 2009
    Eitaro Kurata
    Abstract In this paper, a novel method is proposed for short-term load forecasting, which is one of the important tasks in power system operation and planning. The load behavior is so complicated that it is hard to predict the load. The deregulated power market is faced with the new problem of an increase in the degree of uncertainty. Thus, power system operators are concerned with the significant level of load forecasting. Namely, probabilistic load forecasting is required to smooth power system operation and planning. In this paper, an IVM (Informative Vector Machine) based method is proposed for short-term load forecasting. IVM is one of the kernel machine techniques that are derived from an SVM (Support Vector Machine). The Gaussian process (GP) satisfies the requirements that the prediction results are expressed as a distribution rather than as points. However, it is inclined to be overtrained for noise due to the basis function with N2 elements for N data. To overcome this problem, this paper makes use of IVM that selects necessary data for the model approximation with a posteriori distribution of entropy. That has a useful function to suppress the excess training. The proposed method is tested using real data for short-term load forecasting. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(2): 23, 31, 2009; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/eej.20693 [source]

    Short-term electric power load forecasting using feedforward neural networks

    EXPERT SYSTEMS, Issue 3 2004
    Heidar A. Malki
    Abstract: This paper presents the results of a study on short-term electric power load forecasting based on feedforward neural networks. The study investigates the design components that are critical in power load forecasting, which include the selection of the inputs and outputs from the data, the formation of the training and the testing sets, and the performance of the neural network models trained to forecast power load for the next hour and the next day. The experiments are used to identify the combination of the most significant parameters that can be used to form the inputs of the neural networks in order to reduce the prediction error. The prediction error is also reduced by predicting the difference between the power load of the next hour (day) and that of the present hour (day). This is a promising alternative to the commonly used approach of predicting the actual power load. The potential of the proposed method is revealed by its comparison with two existing approaches that utilize neural networks for electric power load forecasting. [source]

    Electricity peak load forecasting with self-organizing map and support vector regression

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 3 2006
    Shu Fan Non-member
    Abstract This paper aims to study the short-term peak load forecasting (PLF) by using Kohonen self-organizing maps (SOM) and support vector regression (SVR). We first adopt a SOM network to cluster the input data set into several subsets in an unsupervised learning strategy. Then, several SVRs for the next day's peak load are used to fit the training data of each subset in the second stage. In the numerical experiments, data of electricity demand from the New York Independent System Operator (ISO) are used to verify the effectiveness of the prediction for the proposed method. The simulation results show that the proposed model can predict the next day's peak load with a considerably high accuracy compared with the ISO forecasts. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]

    On the use of reactive power as an endogenous variable in short-term load forecasting

    INTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 5 2003
    P. Jorge Santos
    Abstract In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on variables, which are transversal to most models. Among the most significant variables we can find load, expressed as active power (MW), as well as exogenous variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting endogenous variable. The inclusion of this additional variable is mainly due to the evidence that correlations between load and weather variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd. [source]

    Electric load forecasting for northern Vietnam, using an artificial neural network

    OPEC ENERGY REVIEW, Issue 2 2003
    Subhes C. Bhattacharyya
    This paper employs a feed-forward neural network with a back-propagation algorithm for the short-term electric load forecasting of daily peak (valley) loads and hourly loads in the northern areas of Vietnam. A large set of data on peak loads, valley loads, hourly loads and temperatures was used to train and calibrate the artificial neural network (ANN). The calibrated network was used for load forecasting. The mean percentage errors for the peak load, valley load, one-hour-ahead hourly load and 24-hour-ahead hourly load were ,1.47%, ,3.29%, ,2.64% and ,4.39%, respectively. These results compare well with similar studies. [source]