【正文】 next year’ s peak distribution. Figure . Correlation between the actual load and the model. Figure . Convergence of the R2 for the actual load vs the model. The software generates several important characteristics. For example,for each load pocket and for the system, it calculates a weather normalization factor that is a ratio of the peak load to the load that would be observed under average peak conditions. It also produces probability distributions for the next year described methods can be applied to both medium and longterm forecasting. However, the longterm forecasts should incorporate economic and population dynamic forecasts as input parameters. Shortterm load forecasting methods A large variety of statistical and artificial intelligence techniques have been developed for shortterm load forecasting. Similarday approach. This approach is based on searching historical data for days within one, two, or three years with similar characteristics to the forecast day. Similar characteristics include weather, day of the week, and the date. The load of a similar day is considered as a forecast. Instead of a single similar day load, the forecast can be a linear bination or regression procedure that can include several similar trend coefficients can be used for similar days in the previous years. 4. Future Research Directions In this chapter we have discussed several statistical and artificial intelligence techniques that have been developed for short, medium, and longterm electric load forecasting. Several statistical models and algorithms that have been developed though, are operating ad hoc. The accuracy of the forecasts could be improved, if one would study these statistical models and develop mathematical theory that explains the convergence of these should also investigate the boundaries of applicability of the developed models and algorithms. So far, there is no single model or algorithm that is superior for all utilities. The reason is that utility service areas vary in differing mixtures of industrial, mercial, and residential customers. They also vary in geographic, climatologic, economic,and social characteristics. Selecting the most suitable algorithm by a utility can be done by testing the algorithms on real data. In fact,some utility panies use several load forecasting methods in far as we know, nothing is known on a priori conditions that could detect which forecasting method is more suitable for a given load area. An important question is to investigate the sensitivity of the load forecasting algorithms and models to the number of customers, characteristics of the area, energy prices, and other mentioned above, weather is an important factor that influences the load. The usual approach to shortterm load forecasting uses the forecasted weather scenario as an input. However, one of the most important recent developments in weather forecasting is the socalled ensemble approach which consists of puting multiple forecasts. Then probability weights can be assigned to these of using the single weather forecast,weather ensemble predictions can be used as multiple inputs for load forecasts. These inputs generate multiple load forecasts. In recent papers, the authors describe ensemble load predictions based on weather ensembles and various statistical forecasting methods. There are two advantages of having load forecasts in the probabilistic form: (i) they can lead to a more accurate hourly forecast obtained by using multiple ensembles, for example, by averaging them。 Fs 預期目標： 2021 .12 完成翻譯 收集資料 建模 編程 三、設計 (論文 )的研究重點及難點： 重點：建立 灰色模型 及其改進 模型 難點：灰色模型 的數(shù)學建模及其 MATLAB 程序的編寫 四、設計 (論文 )研究方法及步驟 (進度安排 )： 設計研究方法： 以定性分析為主 步驟： 確定負荷預測目的，制訂預測計劃 搜尋、整理、分析資料 建立預測模型、運用 MATLAB 軟件編程及仿真 確定預測結果，分析誤差 編寫預測報告 五、進行設計 (論文 )所需條件： 1． 電力系統(tǒng)短期負荷預測樣本數(shù)據(jù) （ 某市 2021 年 11 月電力負荷實際數(shù)據(jù) 、 該市2021 年 11 月天氣情況 的數(shù)據(jù)） ２．有關負荷預測和灰色理論的期刊和書 籍、 MATLAB 軟件 六、指導教師意見： 簽名： 年 月 日 LOAD FORECASTING Eugene A. Feinberg State University of New York, Stony Brook Dora Gehliou State University of New York, Stony Brook Abstract Load forecasting is vitally important for the electric industry in the deregulated economy. It has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. A large variety of mathematical methods have been developed for load forecasting. In this chapter we discuss various approaches to load forecasting. Keywords: Load, forecasting, statistics, regression, artificial intelligence. 1. Introduction Accurate models for electric power load forecasting are essential to the operation and planning of a utility pany. Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. Load forecasts are extremely important for energy suppliers,ISOs, financial institutions, and other participants in electric energy generation, transmission, distribution, and forecasts can be divided into three categories: shortterm forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and longterm forecasts which are longer than a year. The forecasts for different time horizons are important for different operations within a utility pany. The natures of these forecasts are different as well. For example, for a particular region, it is possible to predict the next day load with an accuracy ofapproximately 13%. However, it is impossible to predict the next year peak load with the simila