Photovoltaic power generation forecasting is an important task in renewable energy power system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic power generation forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) using a data acqui...

This paper is proposing an active framework for forecasting the competitiveness of knowledge based economy (KBE) for nations to serve the public, private, and policy makers. We have used three different forecasting methods including, panel data analysis, artificial neural networks (ANN) and linear multiple regression. By structuring and feeding balanced, panel data to the ANN framework, we were...

Accurate load forecasting is essential to the operation and planning of power systems electricity markets. In this paper, an ensemble radial basis function neural networks (RBFNNs) proposed which trained by minimizing localized generalization error for short-term mid-term forecasting. Exogenous features extracted from series (with long memory multi-resolution wavelet transform) in various times...

Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short ter...

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensembl...

Daily inflow forecasting is of vital importance in reservoir economic operation. In the context hydrometeorological forecasting, effectiveness data-driven models has been demonstrated as bias correctors for physically-based or direct models. However, existing studies only highlight performance improvements provided by model, lacking a comprehensive investigation on whether model should be used ...