The paper presents a spatio-temporal wind speed forecasting algorithm using Deep Learning (DL) and in particular, Recurrent Neural Networks (RNNs). Motivated by recent advances in renewable energy integration and smart grids, we apply our proposed algorithm for wind speed forecasting. Renewable energy resources (wind and solar) are random in nature and, thus, their integration is facilitated wi...

Wind power generation increases rapidly. The available wind energy depends on the wind speed, which is a random variable. For the wind-farm operator, this poses difficulty in the system scheduling and energy dispatching, as the schedule of the wind-power availability is not known in advance. In this research, we propose an intelligent technique for forecasting wind speed and power output of win...

This paper presents a wind speed short-term forecasting from incomplete time series. This study has been proposed given the interest increasing in a global transition of clean energy production. Being

* Corresponding author ([email protected]) Abstract: The performance of artificial neural networks in forecasting short range (3-6 hourly) occurrence of rainfall is presented. Feature sets extracted from both surface level weather parameters and satellite images were used in developing the networks. The study was limited to forecasting the weather over Colombo (79°52’ E, 6°54’ N), the capital...

Utilization ofwind power as one of renewable resources of energy has been growing quickly all over the world in the last decades. Wind power generation is significantly vacillating due to the wind speed alteration. Therefore, assessment of the output power of this type of generators is always associated with some uncertainties. A precise wind power prediction can efficiently uphold transmission...

The modelling of wind speed is a traditional topic in meteorological research, where the main interest is on the short-term forecast of wind speed intensity and direction. More recently, this theme has received some interest in the quantitative finance literature for its relationship with electricity production by wind farms. In fact, electricity producers are interested in long-range forecasts...

This contribution presents the application of feed-forward neural networks to the problem of time series forecasting. This forecast technique is applied to the water flow and wind speed time series. The results obtained from the forecasting of these two renewable resources can be used to determine the power generation capacity of micro or mini-hydraulic plants, and wind parks, respectively. The...

ozone concentration in metropolitan areas frequently exceed regulatory standards now. since it is harmful on human health and environment, its modeling and visualization are of vital important. modeling and prediction of ozone is required by urban managers to control and prevent its effects. in this paper, important parameters influencing the hourly ozone concentration are estimated by using da...

Wind speed high-precision prediction is one of the most important technical aspects to protect the safety of wind power utilization. In this study, two new hybrid methods [FEEMD-MEA-MLP/FEEMD-GA-MLP] are proposed for the wind speed accurate multi-step predictions by combining FEEMD (Fast Ensemble Empirical Mode Decomposition), MEA (Mind Evolutionary Algorithm), GA (Genetic Algorithm) and MLP (M...