Long term electricity load forecasting based on regional load model using optimization techniques: A case study
Citation
Mustafa Şeker (2022) Long term electricity load forecasting based on regional load model using optimization techniques: A case study, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44:1, 21-43, DOI: 10.1080/15567036.2021.1945170Abstract
Long-term load forecasting is a significant and complex topic in electric
distribution systems. Forecasters is need to proper forecasting methodologies
and smart solutions to minimize complexity. In this study, regional longterm
load forecasting is presented, for Sivas province of Turkey, taking into
account the development plan of the municipality, and subscriber profiles.
Firstly, the municipality development plan is divided into regions of similar
load characteristics. The load demand values of each region are defined
mathematically using the S curve. The optimal parameter values of the
S curve are calculated using meta-heuristic methods such as Genetic
Algorithm (GA), Grey Wolf Optimization (GWO) and Harris Hawk
Optimization (HHO). The obtained results are compared with the results of
the econometrics-based (top-to-bottom) approach and actual consumer
projection. The consumption values between 2004 and 2014 are used for
parameter estimation of S curves. The consumption values obtained as the
result of analysis the period between 2015 and 2018 were selected as test
data. The result is shown that S curve-based regional demand forecast
demonstrated more convenient results using the HHO algorithm with statistical
values of RE = 1.3362, MAE = 1.5145, RMSE = 1.80385 and STD = 2.122 can
be applied to the forecast regional electricity consumption. The proposed
method is simple and can be easily applied to forecast the total consumption
of the power load for a province any load forecasting region. The presented
approach can be used to define the future projections of electricity distribution
systems and determine the correct investment strategies.