Load Nowcasting: Predicting Actuals with Limited Data

Abstract

We introduce the problem of load nowcasting to the energy forecasting literature. The recent load of the objective area is predicted based on limited available metering data within this area. Thus, slightly different from load forecasting, we are predicting the recent past using limited available metering data from the supply side of the system. Next, to an industry benchmark model, we introduce multiple high-dimensional models for providing more accurate predictions. They evaluate metered interconnector and generation unit data of different types like wind and solar power, storages, and nuclear and fossil power plants. Additionally, we augment the model by seasonal and autoregressive components to improve the nowcasting performance. We consider multiple estimation techniques based on the lassoand ridge and study the impact of the choice of the training/calibration period. The methodology is applied to a European TSO dataset from 2014 to 2019. The overall results show that in comparison to the industry benchmark, an accuracy improvement in terms of MAE and RMSE of about 60% is achieved. The best model is based on the ridge estimator and uses a specific non-standard shrinkage target. Due to the linear model structure, we can easily interpret the model output.

Estimation Method of the State of 6-10 kV Distribution Network

“…According to Grishkevich [1], all elements of electrical networks are divided into nonrecoverable and recoverable after damage. Non-recoverable elements of electrical networks include a number of elements given in literatures [1][2][3], but we will focus on the reliability indicators of recoverable elements, which include most of the elements of the distribution network [8][9][10][11][12], including transformers (TP), air (OHL) and cable lines (CL).…”

Model and Algorithm of Electricity Consumption Management for Household Consumers in the Republic of Tajikistan

“…As an example, from the point of view of territorial location, the ambient temperature in Dushanbe does not fall below -5℃ in winter, and in the Gorno-Badakhshan Autonomous Region with the center of Khorog, the ambient temperature in winter can reach -30℃, therefore, electricity consumption by household consumers in Khorog will be higher than in Khorog. The influence of temperature on power consumption has been sufficiently studied [8][9][10][11][12].…”

Improving Energy Efficiency by Household Consumers in the Republic of Tajikistan Based on the Developed Forecasting Method

“…The concept of efficiency is understood as the reliability of the power supply. In turn, reliability includes [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]:…”