Power System Load Interval Forecasting

It's been long-termly recognized that power load forecasting is important for many power system departments such as designing, planning, programming, marketing, trading, scheduling and so on. Through a constant exploratory work of domestic and foreign scholars along with experts in power system operation and management for a long time, a series of effective forecasting methods have been developed. However, analysis of the existing load forecasting methods finds that a large number of methods get deterministic forecasting results. In fact, decision-making inevitably has a certain degree of risk because of the various uncertainty factors in power system; therefore, the uncertainty of power demand must be taken into account in decision-making. The outcome of the traditional deterministic forecasting methods cannot reflect the uncertainty of the demand while that of the interval forecasting method is able to meet this objective requirement. The interval forecasting method does not offer a simple determinate forecast, but a range to describe the possible trend of future forecasting result, corresponding to a certain probability confidence level.. According to the results of the interval forecast, the power system decision-makers can make a better understand of the fluctuations and the possible uncertainties of the future load as well as the risk factors it would face, so as to make more reasonable decisions timely. Therefore, it's of great practical and theoretical significance to analyze the power load variation law and study the power load probabilistic forecasting method to realize the power load uncertain prediction.In this paper, on the base of the characteristic analysis of the long-term and short-term power load, along with the identification of the load itself variation and the influence rules of relevant factors, the power load interval forecast models and its solving methods are studied using grey system theory, neural network models and chaotic time series method. The examples verify the accuracy of the interval forecasting results and prove the effectiveness of the algorithm. The research achievements can be applied to the electricity market analysis and forecast system to provide a scientific basis for decision making in power system operation and management. The main research and innovative results are as follows: (1) The traditional grey model GM(1,1) often has great error when forecasting the non-exponential growth curve. In order to solve this problem, the nonlinear gray Bernoulli model (NGBM) is applied to medium- and long-term power load forecasting and a particle swarm optimization (PSO) algorithm is proposed to optimize the parameter of NGBM. Through the verification using different testing data and the forecasting of power load data in actual power system, it is proved that the proposed method possesses better adaptability and higher forecasting accuracy than traditional GM(1,1) and Grey Verhulst model. According to the fact that many factors affect the load, simple linear regression and multiple linear regression were employed to interval load forecasting. And considering lack of history data of related factors, a novel combined method based on simple linear regression and GM(1,1) is used. The interval forecasting results of Fujian province's load demand show that the combined method has a better forecasting effect.(2) Aiming at solving the information loss problem when converting the weather variables to the determinate value in traditional fuzzy clustering analysis method, based on the analysis of the influence law of weather and day type on the short-term load, a new clustering analysis method using interval value is presented.The new method uses the interval value to describe the membership degree of every element in the classification set,and then try to get the similarity of intervals and finally the aggregation.The learning samples are selected by the new fuzzy clustering method and a load forecasting model using the interval arithmetic back-propagation neural network (IABPNN) is established. This model can fully develop the ability of solving uncertainty problem by interval computation and fuzzy theory and the ability of solving nonlinear problems by neural network. It takes the interval value as the input, network outcome as the interval forecasting results, to give the changing range of future power load.(3) Nonlinear dynamical system theory is applied to the modeling and prediction of power load. Prediction accuracy is selected as an identification tool to analyze dynamic characteristics of power load variation. Analysis results of load time series show that the variation of power load can be characterized as a low-dimensional chaotic system. According to chaotic characteristic of power load and the accuracy of one-step forward prediction, the authors propose a new method to implement optimal selection of reconstruction parameters, such as the best embedding dimension and delay time, and use weighted local-region multi-step forecasting model based on phase-space reconstruction to forecast short-term load. Because phase space model can identify the inherent characteristics of power load and can be used in load forecasting, the proposed method is effective in power load analysis and forecasting.(4) According to the chaotic characteristic of power load, a chaotic time series algorithm for short-term load probabilistic interval forecasting is proposed to avoid the error caused by embedding dimension, time delay and similar states extracted method in determinate chaotic forecasting method. First, reconstruct the phase space in the way of searching similar states of current phase point using the clustering algorithm, and determine the interval of the future load values according to the forecasting results of the similar states. Meanwhile, calculate the corresponding probability of the interval on the base of the statistical characters of history forecasting error. The feasibility and effectiveness of the proposed method is evaluated by applying it to a northern power grid.(5) Probabilistic forecasting provides more information than interval forecasting.. In order to meet the demands of uncertain risk analysis and decision-making in electricity market, a probabilistic load forecasting method based on chaotic time series forecasted method is presented. First, the deterministic forecasting results and local predictive variance are obtained using chaotic time series method, and then the distribution and the percentiles of history load forecasting errors is estimated. According to the estimation of the percentiles and local predictive variance, along with the combination of the deterministic load forecasting result, the forecasting interval is constructed and the probabilistic load forecasting results can be obtained. The practicability and validity of the proposed method are tested with the actual data.