most citedProbabilistic Models for Daily Peak Loads at Distribution Feeders

3 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

stat.ML2019

Medium-Term Load Forecasting Using Support Vector Regression, Feature Selection, and Symbiotic Organism Search Optimization

Arghavan Zare-Noghabi, Morteza Shabanzadeh, Hossein Sangrody

An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while…

stat.ML2017

Weather Forecasting Error in Solar Energy Forecasting

Hossein Sangrody, Morteza Sarailoo, Ning Zhou +3

As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy fo…

stat.ML2017

On the Performance of Forecasting Models in the Presence of Input Uncertainty

Hossein Sangrody, Morteza Sarailoo, Ning Zhou +2

Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before.…

cs.AI2017

Reliability Assessment of Distribution System Using Fuzzy Logic for Modelling of Transformer and Line Uncertainties

Ahmad Shokrollahi, Hossein Sangrody, Mahdi Motalleb +3

Reliability assessment of distribution system, based on historical data and probabilistic methods, leads to an unreliable estimation of reliability indices since the data for the d…

cs.CE2017

DG-Embedded Radial Distribution System Planning Using Binary-Selective PSO

Ahvand Jalali, S K. Mohammadi, H. Sangrody +1

With the increasing rate of power consumption, many new distribution systems need to be constructed to accommodate connecting the new consumers to the power grid. On the other hand…

stat.AP20173 cited

Probabilistic Models for Daily Peak Loads at Distribution Feeders

Hossein Sangrody, Ning Zhou, Xingye Qiao

Load forecasting at distribution networks is more challenging than load forecasting at transmission networks because its load pattern is more stochastic and unpredictable. To plan…