Long term electricity load forecasting based on regional load model using optimization techniques: A case study


Şeker M.

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, vol.1, no.0, 2021 (Journal Indexed in SCI)

  • Publication Type: Article / Article
  • Volume: 1 Issue: 0
  • Publication Date: 2021
  • Doi Number: 10.1080/15567036.2021.1945170
  • Title of Journal : ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS

Abstract

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 long-term 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.