Solar Power Forecasting Using Support Vector Regression

03/29/2017
by   Mohamed Abuella, et al.
0

Generation and load balance is required in the economic scheduling of generating units in the smart grid. Variable energy generations, particularly from wind and solar energy resources, are witnessing a rapid boost, and, it is anticipated that with a certain level of their penetration, they can become noteworthy sources of uncertainty. As in the case of load demand, energy forecasting can also be used to mitigate some of the challenges that arise from the uncertainty in the resource. While wind energy forecasting research is considered mature, solar energy forecasting is witnessing a steadily growing attention from the research community. This paper presents a support vector regression model to produce solar power forecasts on a rolling basis for 24 hours ahead over an entire year, to mimic the practical business of energy forecasting. Twelve weather variables are considered from a high-quality benchmark dataset and new variables are extracted. The added value of the heat index and wind speed as additional variables to the model is studied across different seasons. The support vector regression model performance is compared with artificial neural networks and multiple linear regression models for energy forecasting.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

04/27/2017

Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting

To mitigate the uncertainty of variable renewable resources, two off-the...
06/19/2021

Neural network interpretability for forecasting of aggregated renewable generation

With the rapid growth of renewable energy, lots of small photovoltaic (P...
04/25/2018

GRASP: a GReen energy Aware SDN Platform

The transition to renewable energy sources for data centers has become a...
08/02/2018

Impacts of Weather Conditions on District Heat System

Using artificial neural network for the prediction of heat demand has at...
05/14/2021

Study of a Hybrid Photovoltaic-Wind Smart Microgrid using Data Science Approach

In this paper, a smart microgrid implemented in Paracas, Ica, Peru, comp...
11/03/2019

The Importance of Environmental Factors in Forecasting Australian Power Demand

In this paper, a seasonal autoregressive integrated moving average (SARI...
11/28/2020

Machine Intelligent Techniques for Ramp Event Prediction in Offshore and Onshore Wind Farms

Globally, wind energy has lessened the burden on conventional fossil fue...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.