Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability respect the forecasts of loads and generation renewable energies. However, while there are lots approaches accounting uncertainties in these forecasts, their impact rarely analyzed systematically. Here, we use a simplified linear state space model commercial building including photovoltaic (PV) pl...

Most large construction projects face the problem of cost overruns and failures to meet deadlines mainly due changes in building materials. A lot studies proved high importance materials for project budget highlighted a number factors that determine However, modern unstable economic dynamics lead need not only observe sufficient accuracy quantity calculations regarding primary but also carefull...

As a result to today’s uncertain economy, companies are searching for alternative ways to stay competitive. In which, Company XYZ has been faced with an ineffective forecasting method that has lead to multiple product stock outs. The issue faced has caused sales loss as well as profit loss, which companies can not afford to lose if they want to stay competitive. This project goes through the pr...

Modelling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We show that when the kernel these also learned (using flows, variant cross-validation), then resulting data-driven models are not only faster than equation-based but easier to train neural networks long short-term memor...

For operational flood forecasting and operational decision-makers, ready access to current and forecasted meteorological conditions is essential for initiating flood response measures and issuing flood warnings. Effective flood forecasting systems must provide reliable, accurate and timely forecasts for a range of catchments; from small rapidly responding urban areas, to large, more slowly resp...