Effect of Short-term and High-resolution Load Forecasting Errors on Microgrid Operation Costs
Paper in proceeding, 2022

The aim of this paper is to evaluate the effect of the load forecasting errors to the operation costs of a grid-connected microgrid. To this end, a microgrid energy scheduling optimization model was tested with deterministic and stochastic formulations under two solution approaches i.e., day-ahead and rolling horizon optimization. In total, twelve simulation test cases were designed receiving as input the forecasts provided by one of the three implemented machine learning models: linear regression, artificial neural network with backpropagation, and long short-term memory. Simulation results of the weekly operation of a real residential building (HSB Living Lab)
showed no significant differences among the costs of the test cases for a daily mean absolute percentage forecast error of about 12%. These results suggest that operators of similar microgrid systems could use simplifying approaches, such as day-ahead deterministic optimization, and forecasts of similar, non-negligible accuracy without substantially affecting the microgrid's total cost as compared to the ideal case of perfect forecast. Improving the accuracy would mainly reduce the microgrid's peak power cost as shown by its 20.2% increase in comparison to the ideal case.


machine learning


stochastic optimization

load forecasting

energy management


Kyriaki Antoniadou-Plytaria

Chalmers, Electrical Engineering, Electric Power Engineering

Ludvig Eriksson

Student at Chalmers

Jakob Johansson

Student at Chalmers

Richard Johnsson

Student at Chalmers

Lasse Kötz

Student at Chalmers

Johan Lamm

Student at Chalmers

Ellinor Lundblad

Student at Chalmers

David Steen

Chalmers, Electrical Engineering, Electric Power Engineering

Anh Tuan Le

Chalmers, Electrical Engineering, Electric Power Engineering

Ola Carlson

Chalmers, Electrical Engineering, Electric Power Engineering

IEEE PES Innovative Smart Grid Technologies Conference Europe

Vol. 2022-October
978-166548032-1 (ISBN)

2022 IEEE PES ISGT Europe
Novi Sad, Serbia,


European Commission (EC) (EC/H2020/864048), 2019-11-01 -- 2023-04-30.

Subject Categories

Computational Mathematics

Computer Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering





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