A novel data-driven multi-energy load forecasting model

Distributed Generation Expansion Planning Considering Load Growth Uncertainty: A Novel Multi-Period Stochastic Model

Abstract – Distributed generation (DG) technology is known as an efficient solution for applying in distribution system planning (DSP) problems. Load growth uncertainty associated with distribution network is a significant source of uncertainty which highly affects optimal management of DGs. In order to handle this problem, a novel model is proposed in this paper based on DG solution, consideri...

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach

Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in the city of Tehran. Considering the hazards of ozone (O3) gas on human health and the environmen...

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the c...

Flood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique

Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that th...