Ugurlu Umut, Tas Oktay, Kaya Aycan, Oksuz Ilkay
Management Engineering Department, Istanbul Technical University, Besiktas, Istanbul 34367, Turkey.
Industrial Engineering Department, Istanbul Technical University, Besiktas, Istanbul 34367, Turkey.
Energies (Basel). 2018 Aug 11;11(8):2093. doi: 10.3390/en11082093. eCollection 2018 Aug.
Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important loss of profits to the suppliers. In this paper, the financial effect of inaccurate electricity price forecasts on a hydro-based GenCo is examined. Electricity price forecasts of five individual and four hybrid forecast models and the ex-post actual prices are used to schedule the hydro-based GenCo using Mixed Integer Linear Programming (MILP). The financial effect measures of profit loss, Economic Loss Index (ELI) and Price Forecast Disadvantage Index (PFDI), as well as Mean Absolute Error (MAE) of the models are used for comparison of the data from 24 weeks of the year. According to the results, a hybrid model, 50% Artificial Neural Network (ANN)-50% Long Short Term Memory (LSTM), has the best performance in terms of financial effect. Furthermore, the forecast performance evaluation methods, such as Mean Absolute Error (MAE), are not necessarily coherent with inaccurate electricity price forecasts' financial effect measures.
由于要根据电价预测来安排发电计划，电价预测对发电公司（GenCos）有着至关重要的影响。不准确的电价预测可能会给供应商造成重大利润损失。本文研究了不准确的电价预测对一家以水电为主的发电公司的财务影响。使用五个单独的和四个混合预测模型的电价预测以及事后实际价格，通过混合整数线性规划（MILP）来安排以水电为主的发电公司的发电计划。使用利润损失、经济损失指数（ELI）和价格预测劣势指数（PFDI）等财务影响度量，以及模型的平均绝对误差（MAE）来比较一年中24周的数据。根据结果，一个50%人工神经网络（ANN）-50%长短期记忆（LSTM）的混合模型在财务影响方面表现最佳。此外，诸如平均绝对误差（MAE）等预测性能评估方法不一定与不准确的电价预测的财务影响度量相一致。
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