Results 221 to 230 of about 9,504 (261)
A robust solution for power grid management using a hybrid deterministic and probabilistic model for short term load forecasting. [PDF]
Sci RepSerttas F.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Detection, mining and forecasting of impact load in power load forecasting
Applied Mathematics and Computation, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianzhou Wang, Zhixin Ma, Lian Li
openaire +2 more sources
A Fast and Stable Forecasting Model to Forecast Power Load
International Journal of Pattern Recognition and Artificial Intelligence, 2015As the traditional gray forecasting model GM(1, 1) has poor performance in forecasting the fast-growing power load, we present a chaotic co-evolutionary particle swarm optimization (CCPSO) algorithm, one with better efficiency than the PSO algorithm. Based on the GM(1, 1) model, the CCPSO algorithm is adopted to solve the values of parameters a and b ...
Li-Zhi Tan +5 more
openaire +1 more source
Power Load Forecasting Using a Refined LSTM
Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 2019The power load forecasting is based on historical energy consumption data of a region to forecast the power consumption of the region for a period of time in the future. Accurate forecasting can provide effective and reliable guidance for power construction and grid operation. This paper proposed a power load forecasting approach using a two LSTM (long-
Dedong Tang +4 more
openaire +1 more source
Risk adjusted forecasting of electric power load
2014 American Control Conference, 2014Load forecasting of energy demand is usually focused on mean values in related statistical models and ignores rare peak events. This paper provides Extreme Value Theory analysis of the peak events in electrical power load demand. It estimates risk of the peak events by combining forecast of the mean with extreme value modeling of distribution tail. The
Saahil Shenoy, Dimitry M. Gorinevsky
openaire +1 more source
A New Intelligent Method for Power Load Forecasting
Sixth International Conference on Intelligent Systems Design and Applications, 2006Various factors that influence power load are more and more intricate. The traditional load forecasting methods can no longer adapt to the situation. Self-organizing method is a comparably new modeling method and so as to be easily used in the recognition and prediction of complex non-linear systems. Compared with traditional forecasting methods, it is
Wei Li +4 more
openaire +1 more source
Power load forecast system for Turkish electric market
2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015Forecasting the electric load demand in advance is very important in deregulated market conditions to give proper production, purchase, maintenance and investment decisions. Correct price forecasts also depend on accurate load prediction. In this study, effects of calendar, historical price and load data on short-term load forecast for Turkish ...
Ömer Özgür Bozkurt +2 more
openaire +2 more sources
Research on load forecasting model on power sensor net
2020 16th International Conference on Mobility, Sensing and Networking (MSN), 2020The business process of load forecasting algorithm for distribution network planning is studied in depth, and the work steps of load forecasting in different places are discussed according to different planning objectives. Then, based on power sensor net, we systematically describes various power demand forecasting models and related theories, as well ...
Kai Huang +5 more
openaire +1 more source
The Power Load Forecasting by Kernel PCA
2010We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3 ...
Fang-Tsung Liu +3 more
openaire +1 more source