scholarly journals Short-Term Electricity Demand Forecasting Using a Functional State Space Model

Energies ◽  
2018 ◽  
Vol 11 (5) ◽  
pp. 1120 ◽  
Author(s):  
Komi Nagbe ◽  
Jairo Cugliari ◽  
Julien Jacques
Keyword(s):  
State Space ◽  
Functional State ◽  
Demand Forecasting ◽  
State Space Model ◽  
Electricity Demand ◽  
Short Term ◽  
Space Model ◽  
Electricity Demand Forecasting

scholarly journals Multiple-Input Single-Output Polynomial Nonlinear State-Space Model of the Li-ion Battery’s Short-term Dynamics

2018 ◽  
Vol 51 (15) ◽  
pp. 497-502
Author(s):  
Rishi Relan ◽  
Koen Tiels ◽  
Jean-Marc Timmermans ◽  
Johan Schoukens
Keyword(s):  
State Space ◽  
State Space Model ◽  
Short Term ◽  
Space Model ◽  
Single Output ◽  
Multiple Input ◽  
Li Ion ◽  
Nonlinear State

scholarly journals A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1596 ◽  
Author(s):  
Xin Zhao ◽  
Haikun Wei ◽  
Chenxi Li ◽  
Kanjian Zhang
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
State Space ◽  
State Space Model ◽  
Hybrid Approach ◽  
Wind Farms ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Space Model ◽  
Wind Speeds

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

Short-Term Oil Price Forecasting Based on State Space Model

2011 ◽  
Vol 403-408 ◽  
pp. 2530-2534
Author(s):  
Wei Qi Li ◽  
Lin Wei Ma ◽  
Ya Ping Dai ◽  
Dong Hai Li
Keyword(s):  
State Space ◽  
Likelihood Function ◽  
State Space Model ◽  
The State ◽  
Oil Price ◽  
Filter Method ◽  
Parameter Estimates ◽  
Price Forecasting ◽  
Short Term ◽  
Space Model

In competitive petroleum markets, oil price forecasting has always been an important strategic tool for oil producers and consumers to predict market behavior. In this study, we researched the monthly crude oil price in the period between 1988 and 2009. Firstly, we present a state space model to represent oil price system. Secondly, we determine the parameter estimates of the state space model for oil price through a faster algorithm to compute the likelihood function. Lastly, we use the Kalman filter method to estimate the next three months’ oil price and compare it with the econometric structure model as a benchmark. Empirical results indicate that the state space model performs well in terms of some standard statistics indices, and it may be a promising method for short-term oil price forecasting.

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