09-04-2016, 09:51 AM
plz send me short term load forecasting matlab code
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short term load forecasting matlab code wiki
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plz send me short term load forecasting matlab code
13-04-2016, 09:51 AM
short term load forecasting matlab code wiki
Abstract
This paper presents the development and application of advanced neural networks to face successfully the problem of the short term electric load forecasting. Several approaches including Gaussian encoding backpropagation (BP), window random activation,radial basis function networks, real-time recurrent neural networks and their innovative variations are proposed, compared and discussed in this paper. The performance of each presented structure is evaluated by means of an extensive simulation study, using actual hourly load data from the power system of the island of Crete, in Greece. The forecasting error statistical results, corresponding to the minimum and maximum load time-series, indicate that the load forecasting models proposed here provide significantly more accurate forecasts, compared to conventional autoregressive and BP forecasting models. Finally, a parallel
processing approach for 24 h ahead forecasting is proposed and applied. According to this procedure, the requested load for each specific hour is forecasted, not only using the load time-series for this specific hour from the previous days, but also using the forecasted load data of the closer previous time steps for the same day. Thus, acceptable accuracy load predictions are obtained without the need of weather data that increase the system complexity, storage requirement and cost.
Develop and deploy algorithms for accurate electricity load forecasting
Power companies rely on accurate electricity load forecasting to minimize financial risk and optimize operational efficiency and reliability.
Critical load forecasting tasks include:
Automating data access from regional wholesale electricity markets
Customizing models using nonlinear regression, nonparametric, and neural network techniques
Calibrating models with historical predictors such as weather, seasonality, load, fuel price, and power price
Deploying and integrating load forecasting algorithms into enterprise systems
Abstract
This paper presents the development and application of advanced neural networks to face successfully the problem of the short term electric load forecasting. Several approaches including Gaussian encoding backpropagation (BP), window random activation,radial basis function networks, real-time recurrent neural networks and their innovative variations are proposed, compared and discussed in this paper. The performance of each presented structure is evaluated by means of an extensive simulation study, using actual hourly load data from the power system of the island of Crete, in Greece. The forecasting error statistical results, corresponding to the minimum and maximum load time-series, indicate that the load forecasting models proposed here provide significantly more accurate forecasts, compared to conventional autoregressive and BP forecasting models. Finally, a parallel
processing approach for 24 h ahead forecasting is proposed and applied. According to this procedure, the requested load for each specific hour is forecasted, not only using the load time-series for this specific hour from the previous days, but also using the forecasted load data of the closer previous time steps for the same day. Thus, acceptable accuracy load predictions are obtained without the need of weather data that increase the system complexity, storage requirement and cost.
Develop and deploy algorithms for accurate electricity load forecasting
Power companies rely on accurate electricity load forecasting to minimize financial risk and optimize operational efficiency and reliability.
Critical load forecasting tasks include:
Automating data access from regional wholesale electricity markets
Customizing models using nonlinear regression, nonparametric, and neural network techniques
Calibrating models with historical predictors such as weather, seasonality, load, fuel price, and power price
Deploying and integrating load forecasting algorithms into enterprise systems
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