In this paper, we show that the Chapman-Kolmogorov formula could be used as a recursive formula for computing the m-step-ahead conditional density of a Markov bilinear model. The stationary marginal probability density function of the model may be approximated by the m-step-ahead conditional density for sufficiently large m.

Traditional electricity price forecasting tends to adopt time-domain methods based on time series, which fail make full use of the regional information market, and ignore extra-territorial factors affecting within region under cross-regional transmission conditions. In order improve accuracy forecasting, this paper proposes a novel spatio-temporal prediction model, is combined with graph convol...

This paper addresses the estimation of household communities' overall energy usage and solar production, considering different prediction horizons. Forecasting electricity demand generation communities can help enrich information available to grid operators better plan their short-term supply. Moreover, households will increasingly need know more about patterns make wiser decisions on appliance...

Dynamic Bayesian network is the extension of Bayesian network in solving time series problems .It can be well dealt with the time-varying multivariable problem. A state model is given based on Dynamic Bayesian network. The model can more accurately describe the relationship between the system state and the influencing factors. Single-step and multi-step prediction algorithms are given to predic...

The paper addresses the problem of designing robust output feedback model predictive control to one-step ahead prediction horizon which ensures a parameter dependent quadratic stability and guaranteed cost for the case of linear polytopic systems.

Abstract The conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network demonstrated a powerful ability to tackle time-series predictions. This study proposed novel hybrid by combining the Xinanjiang (XAJ) and LSTM (XAJ-LSTM) achieve precise multi-step-ahead forecasts. takes forecasts of XAJ as input variables enhance physical mech...

In this study, a novel general multi-step ahead strategy is developed for forecasting time series of air pollutants. The values the predictors at future moments are gathered from official weather forecast sites as independent ex-ante data. They updated with new forecasted every day. Each sample used to build- separate single model that simultaneously predicts pollution levels. sought forecasts ...