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Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and the weighted support vector machines. The combined model can effectively count for the seasonality and nonlinearity shown in the electric load...
In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: a hybrid evolutionary algorithm which combines PSO and Artificial Fish Swarm Algorithm Search approach based on test-sample error estimate criterion (PSO-AFSAS-TEE) and support vector regression...
Electricity demand forecasting is an important index to make power development plan and dispatch the loading of generating units in order to meet system demand. In order to improve the accuracy of the forecasting, we apply the feedforward neural network for electricity demand forecasting. Inspired by the idea of artificial fish swarm algorithm, in this paper we proposed one hybrid evolutionary algorithm...
Short-term electricity demand forecasting for the next hour to several days out is one of the most important tools by which an electric utility plans and dispatches the loading of generating units in order to meet system demand. But there exists chaos in electricity systems to a great extent. Complicated electricity systems are nonlinear systems and the forecasting is very complex in nature and quite...
Forecasting electricity consumption is an important index for system planning, operation and decision making. In order to improve the accuracy of the forecasting, we apply an integrated architecture to optimize the prediction. Based on an integration of two machine learning techniques: artificial fish swarm algorithm search approach based on test-sample error estimate criterion (AFSAS-TEE) and support...
People often try to smooth or eliminate impact load all together in traditional power load forecasting. This, however, could result in the loss of important hidden information. In other words, the impact load itself may be of particular important and they cause electric network collapse very easily. So there is a beforehand estimate to change and characteristic of power load, especially in impact...
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