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This paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise and sunset times. The new methodology includes properly treated daylight information in STLF models in order to reduce the forecasting error during sunrise and sunset, especially when daylight savings time (DST) one-hour time shifts occur. This paper describes the raw information and the linearization method needed. The forecasting model used as the benchmark is currently used at the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components. The method has been designed with data from the Spanish electric system from 2011 to 2017 and tested over 2018 data. The results include a justification to use the proposed linearization over other techniques as well as a thorough analysis of the forecast results yielding an error reduction in sunset hours from 1.56% to 1.38% for the AR model and from 1.37% to 1.30% for the combined forecast. In addition, during the weeks in which DST shifts are implemented, sunset error drops from 2.53% to 2.09%.
Miguel López; Sergio Valero; Carlos Sans; Carolina Senabre. Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy. Energies 2020, 14, 95 .
AMA StyleMiguel López, Sergio Valero, Carlos Sans, Carolina Senabre. Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy. Energies. 2020; 14 (1):95.
Chicago/Turabian StyleMiguel López; Sergio Valero; Carlos Sans; Carolina Senabre. 2020. "Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy." Energies 14, no. 1: 95.
Short-Term Load Forecasting is a very relevant aspect in managing, operating or participating an electric system. From system operators to energy producers and retailers knowing the electric demand in advance with high accuracy is a key feature for their business. The load series of a given system presents highly repetitive daily, weekly and yearly patterns. However, other factors like temperature or social events cause abnormalities in this otherwise periodic behavior. In order to develop an effective load forecasting system, it is necessary to understand and model these abnormalities because, in many cases, the higher forecasting error typical of these special days is linked to the larger part of the losses related to load forecasting. This paper focuses on the effect that several types of special days have on the load curve and how important it is to model these behaviors in detail. The paper analyzes the Spanish national system and it uses linear regression to model the effect that social events like holidays or festive periods have on the load curve. The results presented in this paper show that a large classification of events is needed in order to accurately model all the events that may occur in a 7-year period.
Miguel Lopez; Carlos Sans; Sergio Valero; Carolina Senabre. Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study. Energies 2019, 12, 1253 .
AMA StyleMiguel Lopez, Carlos Sans, Sergio Valero, Carolina Senabre. Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study. Energies. 2019; 12 (7):1253.
Chicago/Turabian StyleMiguel Lopez; Carlos Sans; Sergio Valero; Carolina Senabre. 2019. "Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study." Energies 12, no. 7: 1253.
Artificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF) in the last 20 years and it has partly displaced older time-series and statistical methods to a second row. However, the STLF problem is very particular and specific to each case and, while there are many papers about AI applications, there is little research determining which features of an STLF system is better suited for a specific data set. In many occasions both classical and modern methods coexist, providing combined forecasts that outperform the individual ones. This paper presents a thorough empirical comparison between Neural Networks (NN) and Autoregressive (AR) models as forecasting engines. The objective of this paper is to determine the circumstances under which each model shows a better performance. It analyzes one of the models currently in use at the National Transport System Operator in Spain, Red Eléctrica de España (REE), which combines both techniques. The parameters that are tested are the availability of historical data, the treatment of exogenous variables, the training frequency and the configuration of the model. The performance of each model is measured as RMSE over a one-year period and analyzed under several factors like special days or extreme temperatures. The AR model has 0.13% lower error than the NN under ideal conditions. However, the NN model performs more accurately under certain stress situations.
Miguel López; Carlos Sans; Sergio Valero; Carolina Senabre. Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting. Energies 2018, 11, 2080 .
AMA StyleMiguel López, Carlos Sans, Sergio Valero, Carolina Senabre. Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting. Energies. 2018; 11 (8):2080.
Chicago/Turabian StyleMiguel López; Carlos Sans; Sergio Valero; Carolina Senabre. 2018. "Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting." Energies 11, no. 8: 2080.