外文翻譯--電力負(fù)荷預(yù)測(cè)方法決策工具

1、章及標(biāo)題 附錄 3Electric load forecasting methods: Tools for decision making Heiko Hahn, Silja Meyer-Nieberg *, Stefan PicklFakult für Informatik, der Bundeswehr, 85577 Neubiberg, Germany Univer

2、sitat ??Abstract For decision makers in the electricity sector, the decision process is complex with several different levels that have to be taken into consideration. These comprise for instance the planning of facili

3、ties and an optimal day-to-day operation of the power plant. These decisions address widely different time- horizons and aspects of the system. For accomplishing these tasks load forecasts are very important. Therefore

4、, finding an appropriate approach and model is at core of the decision process. Due to the deregulation of energy markets, load forecasting has gained even more importance. In this article, we give an overview over th

5、e various models and methods used to predict future load demands.2009 Elsevier B.V. All rights reserved.1. Load forecasts in deregulated marketsDecision making in the energy sector has to be based on accurate forecasts

6、of the load demand. Therefore, load forecasts are important tools in the energy sector. Forecasts of different time horizons and different accuracy are needed for the operation of plants and of the complex power syste

7、m itself: The ‘‘system response follows closely the load requirement” (Kyriakides and Polycarpou, 2007, p. 392). The decision maker is faced with a multitude of decision problems on different time-scales as well as on

8、different hierarchies of the power system: These problems comprise for instance the determination of an optimal secure scheduling of unit commitment and energy allocation. But decisions do not have made only with resp

9、ect to the day-to-day operation of the power system but also with respect to investment decisions on new facilities based on the anticipation of future energy demands. For both ends, reliable forecasts are needed. The

10、 deregulation of energy markets has increased the need for accurate forecasts even more (see e.g. Feinberg and Genethliou, 2005; Kyriakides and Polycarpou, 2007). To participate in the market, a player needs an accurate

11、 estimate how much energy is needed at a certain time. On the one hand, an underestimation of the energy demand by a supplier may lead to high operational costs because the additional demand has to be met by procuring

12、 energy in the market. An overestimation on the other hand wastes scarce resources (see e.g. Tzafestas and Tzafestas, 2001; Feinberg and Genethliou, 2005; Kyriakides and Polycarpou, 2007). Furthermore, demand is one o

13、f the main factors for pricing. Load forecasting is therefore at the core of nearly all decisions made in energy markets. Due to the high importance of accurate load forecasting, the history of 章及標(biāo)題

14、 prediction. There is a common agreement that the air temperature is the most important weather influence (see e.g. Hippert et al., 2001; Feinberg and Genethliou, 2005). This was already recognized in the 1930s

15、 (Hippert et al., 2001). Generally, the demand is high on cold days which can be attributed to electric heating. Similarly on hot days, the increased usage of air-conditioning generates a higher demand of energy. In m

16、any countries, this results in a U- shaped and clearly non-linear response function of the load towards the temperature (Hippert et al., 2001). However, the exact shape of the curve depends on the region, the climatic

17、conditions and of course on the consumers’ behavior.Additionally, the designated time-horizon and the availability of the data determine the input variables. As mentioned in (Taylor et al., 2006) univariate models are

18、 standard for very short-term load forecasts for up to 6 hours ahead. Furthermore, it should be noted that sometimes obtaining accurate weather forecasts may be difficult. Therefore, univariate models are also applied

19、for longer lead times (Taylor et al., 2006; Soares and Souza, 2006).In Kyriakides and Polycarpou (2007) three main groups of input data for short-term load forecasts are identified: seasonal input variables, weather f

20、orecast variables, and historical load data (Kyriakides and Polycarpou, 2007). Short-term load forecasts usually aim at providing the daily, hourly, or half- hourly load and the peak load (day, week) (see e.g. Tzafestas

21、 and Tzafestas, 2001) although even smaller time intervals occur. Forecasting the load profile, i.e., the load of the next 24 hours, is also a main target (Tzafestas and Tzafestas, 2001; Hippert et al., 2001).Medium-t

22、erm load forecasts usually incorporate several additional influences - especially demographic and economic factors. These forecasts often provide the daily peak and average load, although hourly loads are also sometime

23、s given, e.g. Bruhns et al. (2005). In the case of long-term load forecasts, even more indicators for the demographic and economic development have to be taken into account (Kyriakides and Polycarpou, 2007). These are

24、 for instance the population growth and the gross domestic product. Long-term load forecasting usually aims at predicting the annual load and the peak load (Kyriakides and Polycarpou, 2007).The time series of the load

25、s itself has generally three seasonal cycles: an intra- daily cycle (the daily load curve or the load profile), a weekly cycle, and a yearly seasonal cycle. The weekly cycle usually shows two main groups: week-days and

26、 weekends. Due to industrial demand, the load tends to be higher during week-days. The weekend tends to influence the neighboring days so that Mondays and Fridays are often treated separately. Saturday is also often fo

27、und to show a different load profile than Sunday. However, the exact weekly pattern depends on the particular region under consideration and furthermore on the season (Hippert et al., 2001). Additionally ‘‘regular” ex