02-02-2010, 09:54 PM
Load forecasting
02-02-2010, 09:54 PM
Load forecasting
03-02-2010, 07:35 AM
i hope you mean electrical load forecasting...
Abstract Load forecasting is the ability of calculating the amount of energy that can be used for different applications such as commercials , industrials , municipals and Load forecasting has 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.. read http://citeseerx.ist.psu.edu/viewdoc/dow...1&type=pdf http://www.ecse.rpi.edu/~chowj/Feinberg.ppt
23-10-2010, 07:08 PM
sir i am doing on project on neural network based load forecasting in matlab software. can you send me the details
thank you sir can you send me the simple model of load forecasting in matlab
01-04-2014, 11:43 AM
LOAD FORECASTING
LOAD FORECASTING.pdf (Size: 352.32 KB / Downloads: 190) Abstract Load forecasting is vitally important for the electric industry in the deregulated economy. It has many applications including energy pur- chasing and generation, load switching, contract evaluation, and infras- tructure development. A large variety of mathematical methods have been developed for load forecasting. In this chapter we discuss various approaches to load forecasting. Introduction Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasting helps an electric utility to make important decisions including decisions on pur- chasing and generating electric power, load switching, and infrastructure development. Load forecasts are extremely important for energy suppli- ers, ISOs, financial institutions, and other participants in electric energy generation, transmission, distribution, and markets. Important Factors for Forecasts For short-term load forecasting several factors should be considered, such as time factors, weather data, and possible customers’ classes. The medium- and long-term forecasts take into account the historical load and weather data, the number of customers in different categories, the appliances in the area and their characteristics including age, the eco- nomic and demographic data and their forecasts, the appliance sales data, and other factors. The time factors include the time of the year, the day of the week, and the hour of the day. There are important differences in load be- tween weekdays and weekends. The load on different weekdays also can behave differently. For example, Mondays and Fridays being adjacent to weekends, may have structurally different loads than Tuesday through Thursday. This is particularly true during the summer time. Holidays are more difficult to forecast than non-holidays because of their relative infrequent occurrence. Forecasting Methods Over the last few decades a number of forecasting methods have been developed. Two of the methods, so-called end-use and econometric ap- proach are broadly used for medium- and long-term forecasting. A variety of methods, which include the so-called similar day approach, various regression models, time series, neural networks, expert systems, fuzzy logic, and statistical learning algorithms, are used for short-term forecasting. The development, improvements, and investigation of the appropriate mathematical tools will lead to the development of more accurate load forecasting techniques. Statistical approaches usually require a mathematical model that rep- resents load as function of different factors such as time, weather, and customer class. The two important categories of such mathematical models are: additive models and multiplicative models. They differ in whether the forecast load is the sum (additive) of a number of compo- nents or the product (multiplicative) of a number of factors. Medium- and long-term load forecasting methods The end-use modeling, econometric modeling, and their combinations are the most often used methods for medium- and long-term load fore- casting. Descriptions of appliances used by customers, the sizes of the houses, the age of equipment, technology changes, customer behavior, and population dynamics are usually included in the statistical and sim- ulation models based on the so-called end-use approach. In addition, economic factors such as per capita incomes, employment levels, and electricity prices are included in econometric models. These models are often used in combination with the end-use approach. Long-term fore- casts include the forecasts on the population changes, economic devel- opment, industrial construction, and technology development. Conclusions Accurate load forecasting is very important for electric utilities in a competitive environment created by the electric industry deregulation. In this paper we review some statistical and artificial intelligence tech- niques that are used for electric load forecasting. We also discussed fac- tors that affect the accuracy of the forecasts such as weather data, time factors, customer classes, as well as economic and end use factors. Load forecasting methods use advanced mathematical modeling. Additional progress in load forecasting and its use in industrial applications can be achieved by providing short-term load forecasts in the form of proba- bility distributions rather than the forecasted numbers; for example the so-called ensemble approach can be used. We believe that the progress in load forecasting will be achieved in two directions: (i) basic research in statistics and artificial intelligence and (ii) better understanding of the load dynamics and its statistical properties to implement appropriate models. |
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