-The load forecasting is required in power system management and ensures electricity providing for customers. Photovoltaic power forecasting aims to reduce the fuel consumption and play important role in the supervisory control for a hybrid energy system. This paper presents the application of new model using neural networks (NN) and Particle Swarm Optimization (PSO) to determine the net load f...

People's affective forecasts are often inaccurate because they tend to overestimate how they will feel after an event. As life decisions are often based on affective forecasts, it is crucial to find ways to manage forecasting errors. We examined the impact of a fair treatment on forecasting errors in candidates in a Belgian reality TV talent show. We found that perceptions of fair treatment inc...

Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as...

A model forecasting ionospheric disturbances and its impact on GNSS positioning is proposed, called HAPEE (High lAtitude Positioning Error Estimator). It allows predicting ROTI index corresponding Precise Point (PPP) error in Arctic region (i.e. latitudes > 50°). The for the next hour a probability of disturbance or PPP to exceed given threshold, from solar wind conditions measured at L1 Lag...

This paper investigates the nature of model error in complex deterministic nonlinear systems, such as weather forecasting models. Forecasting systems incorporate two components, a forecast model and a data assimilation method. The latter projects a collection of observations of reality into a model state. Key features of model error can be understood in terms of geometric properties of the data...

The ionosphere is a notable source of error that disrupts the accuracy of the global position system (GPS) signal to the ground by changing the speed and direction of the signal propagation and in the process causing a delay in the signal. Therefore, forecasting the ionospheric delay is very important to reduce the GPS positioning error. In this work, statistical Holt-Winter method was chosen d...

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recu...

In order to study the prediction ability of combined model based on BP neural network, the information is gathered to established BP neural network model. The BP neural network model is built between the incidence seniority and the influencing factors and whose prediction performance is compared with the traditional model of multivariate and linear regression. Based on this we can set up linear...