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Accurate photovoltaic (PV) power forecasting is of great significance for safe and stable operation for PV power plant and reasonable dispatching of power grids, and it is also a fundamental technology to ensure the high ratio of PV power generation access to the power grid, but its forecasting error cannot meet the actual requirement. Based on data analysis from a 10 kW distributed PV power plant and a 60 MW large-scale power plant, this study shows the PV power short-term forecasting error mainly comes from the numerical weather prediction (NWP) and forecasting process. To improve the forecasting performance, a short-term PV power forecasting method based on irradiance correction and error forecasting is proposed. First, the measured irradiance is used to correct the irradiance in the NWP. Then, the particle swarm optimization (PSO) algorithm is used to optimize the forecasting model. Finally, an error forecasting model is introduced to correct the forecasting result further. It is proved that the proposed method can effectively improve the short-term forecasting accuracy through verification with an actual power plant. Taking the 6 steps correction as an example, the NWP correction can reduce RMSE(1) by 3.0%, the model optimization can reduce RMSE(2) by 0.4%, and the error correction can reduce RMSE by 0.4%.

Water consumption and resulting sewage volume (both strongly impacted by meteorological parameters) are of key importance for an efficient and sustainable operation of waterworks and sewage treatment plants. Therefore, the objective of this research is to analyze the potential impact of input variables uncertainty on the performance of sewage volume forecasting model. The research is based on a real, three-years long, daily time series collected from Torun (Poland). The used time series encompassed: sewage volume, water consumption, rainfall, temperature, precipitation, evaporation, sunshine duration and precipitation at a six hours interval. As a forecasting tool a multi-layer perceptron artificial neural network has been selected. First a simulation model for sewage volume was created which considered above mentioned earlier time series as exogenous variables. Further its performance was tested assuming that all non-historical input variables are prone to their individual forecasting errors. Analysis was dedicated firstly to each variable individually and later the potential of all of them being uncertain was tested. A lack of correlation between input variables error was assumed. The research provides an interesting solution for visualizing the quality and actual performance of forecasting models where some or all of input variables has to be forecasted.

Water consumption and the resulting sewage volume (both strongly impacted by meteorological parameters) are of key importance for an efficient and sustainable operation of waterworks and wastewater treatment plants. Therefore, the objective of this research is to analyze the potential impact of input variables uncertainty on the performance of sewage volume forecasting model. The research is based on a real, three-year long, daily time series collected from Toruń (Poland). The used time series encompassed: sewage volume, water consumption, rainfall, temperature, precipitation, evaporation, sunshine duration and precipitation at a six hours interval. Neural network has been selected as a forecasting tool a multi-layer perceptron artificial., a simulation model for the sewage volume was created which considered the above-mentioned time series as exogenous variables. Further, its performance was tested assuming that all non-historical input variables are prone to their individual forecasting errors. The analysis was dedicated firstly to each variable individually and later the potential of all of them being uncertain was tested. A lack of correlation between the input variables error was assumed. The research provides an interesting solution for visualizing the quality and actual performance of forecasting models where some or all of input variables has to be forecast. [ABSTRACT FROM AUTHOR]

In this paper, we discussed the Statistical modeling of the original data series and the residuals series. Residual series has been use for the forecasting the shock occurring in the economic data series. Objective and Subjective technique has been used for the modeling.

With recent advances in measurement and information technology, there is an abundance of data available for analysis and modelling of hydrodynamic systems. Spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques have resulted in more favourable acceptance by the hydrodynamic community. The data mining tools and techniques are being applied in variety of hydro-informatics applications ranging from data mining for pattern discovery to data driven models and numerical model error correction. The present study explores the feasibility of applying mutual information theory by evaluating the amount of information contained in observed and prediction errors of non-tidal barotropic numerical modelling (i.e. assuming that the hydrodynamic model, available at this point, is best representation of the physics in the domain of interest) by relating them to variables that reflect the state at which the predictions are made such as input data, state variables and model output. In addition, the present study explores the possibility of employing ‘genetic programming’ (GP) as an offline data driven modelling tool to capture the sea level anomaly (SLA) dynamics and then using them for updating the numerical model prediction in real time applications. These results suggest that combination of data relationship analysis and GP models helps to improve the forecasting ability by providing information of significant predicative parameters. It is found that GP based SLA prediction error forecast model can provide significant improvement when applied as data assimilation schemes for updating the SLA prediction obtained from primary hydrodynamic models. [ABSTRACT FROM AUTHOR]

Abstract: A new robust, accurate and efficient data assimilation procedure based on a general filtering update combined with error forecasting at measurement points is presented. The filtering update procedure is based on a predefined, time invariant weighting (gain) function that is used to distribute model errors at measurement points to the entire state of the river system. The error forecast models are used to propagate model errors at measurement points in the forecast period. The procedure supports a general linear and non-linear formulation of the error forecast models, and fully automatic parameter estimation techniques have been implemented to estimate the parameters of the models based on the observed model errors prior to the time of forecast. The parameter estimates are automatically updated, which allow the error forecast models to adapt to the prevailing conditions at the time of forecast, hence accounting for any structural differences in the model errors in the transition between different flow regimes. The developed procedure is demonstrated in an operational flood forecasting setup of Metro Manila, the Philippines. The results showed significantly improved forecast skills for lead times up to 24h as compared to forecasting without updating. Erroneous conditions imposed at the downstream boundary were effectively corrected by utilising the harmonic behaviour of the model error in the error forecast model; a situation where the usually applied autoregressive error forecast models would fail. [Copyright &y& Elsevier]