energies-logo

Journal Browser

Journal Browser

Special Issue "Short-Term Load Forecasting 2021"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Electrical Engineering".

Deadline for manuscript submissions: closed (31 December 2021).

Special Issue Editors

Prof. Dr. Antonio Gabaldón
E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad Politecnica de Cartagena, Cartagena, Spain
Interests: analysis of electrical distribution systems; electricity markets; demand response; energy efficiency; electric haulage in railways and non-invasive monitoring techniques
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dr. María Carmen Ruiz-Abellón
E-Mail Website
Guest Editor
Department of Applied Mathematics and Statistics, Universidad Politecnica de Cartagena, Cartagena, Spain
Interests: forecasting techniques; machine learning; time series analysis; entropy measures; energy markets
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Alfredo Fernández-Jiménez
E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad de La Rioja, La Rioja, Spain
Interests: electricity markets; energy forecasting models; power systems planning; renewables; grid integration of distributed energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is well known that short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies for power system (planning, scheduling, maintenance, and control processes, among others), and this topic has been an important issue for decades. However, there is still so much to do in this field. The deployment of enabling technologies (e.g., smart meters) has made high granular data available for many customer segments and for many tasks, for instance, to make feasible load forecasting tasks at several demand aggregation levels. The first challenge is the improvement of the STLF models and their performance at new demand aggregation levels. Moreover, the increasing inclusion of renewable energies (wind and solar power) in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the near future.

Many techniques have been proposed for STLF, including traditional statistical models (such as SARIMA, ARMAX, exponential smoothing, linear and non-linear models, etc.) and artificial intelligence techniques (such as fuzzy regression, artificial neural networks, support vector regression, tree-based regression, ensemble methods, stacked methods, etc.). Furthermore, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new uncertainties sources in the power system, has given more importance to probabilistic load forecasting in recent years.  

This Special Issue is concerned with both the fundamental research on STLF methodologies and practical application research, in order to face the future challenges of a more distributed power system in the future.

All of the submitted contributions must be based on the rigorous motivation of the mentioned approaches, and demonstrate a theoretically sound framework; submissions lacking such a scientific approach are discouraged. It is reccomended that existing/presented approaches are validated using real practical applications.

Prof. Dr. Antonio Gabaldón
Prof. Dr. Dr. María Carmen Ruiz-Abellón
Prof. Dr. Luis Alfredo Fernández-Jiménez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • short term load forecasting and distributed energy resources
  • short term load forecasting and demand aggregation levels
  • statistical forecasting models (SARIMA, ARMAX, exponential smoothing, linear and non-linear regression, and so on)
  • artificial neural networks (ANNs)
  • fuzzy regression models
  • tree-based regression methods
  • stacked and ensemble methods
  • evolutionary algorithms
  • deep learning architectures
  • support vector regression (SVR)
  • robust load forecasting
  • hierarchical and probabilistic forecasting
  • hybrid and combined models

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation
Energies 2022, 15(1), 128; https://doi.org/10.3390/en15010128 - 24 Dec 2021
Viewed by 261
Abstract
With the rapid expansion of renewable energy, the penetration rate of behind-the-meter (BTM) solar photovoltaic (PV) generators is increasing in South Korea. The BTM solar PV generation is not metered in real-time, distorts the electric load and increases the errors of load forecasting. [...] Read more.
With the rapid expansion of renewable energy, the penetration rate of behind-the-meter (BTM) solar photovoltaic (PV) generators is increasing in South Korea. The BTM solar PV generation is not metered in real-time, distorts the electric load and increases the errors of load forecasting. In order to overcome the problems caused by the impact of BTM solar PV generation, an extreme gradient boosting (XGBoost) load forecasting algorithm is proposed. The capacity of the BTM solar PV generators is estimated based on an investigation of the deviation of load using a grid search. The influence of external factors was considered by using the fluctuation of the load used by lighting appliances and data filtering based on base temperature, as a result, the capacity of the BTM solar PV generators is accurately estimated. The distortion of electric load is eliminated by the reconstituted load method that adds the estimated BTM solar PV generation to the electric load, and the load forecasting is conducted using the XGBoost model. Case studies are performed to demonstrate the accuracy of prediction for the proposed method. The accuracy of the proposed algorithm was improved by 21% and 29% in 2019 and 2020, respectively, compared with the MAPE of the LSTM model that does not reflect the impact of BTM solar PV. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2021)
Show Figures

Figure 1

Article
Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings
Energies 2021, 14(18), 5831; https://doi.org/10.3390/en14185831 - 15 Sep 2021
Cited by 1 | Viewed by 456
Abstract
In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the [...] Read more.
In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2021)
Show Figures

Figure 1

Article
Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights
Energies 2021, 14(11), 3224; https://doi.org/10.3390/en14113224 - 31 May 2021
Cited by 1 | Viewed by 725
Abstract
Forecasting time series with multiple seasonal cycles such as short-term load forecasting is a challenging problem due to the complicated relationship between input and output data. In this work, we use a pattern representation of the time series to simplify this relationship. A [...] Read more.
Forecasting time series with multiple seasonal cycles such as short-term load forecasting is a challenging problem due to the complicated relationship between input and output data. In this work, we use a pattern representation of the time series to simplify this relationship. A neural network trained on patterns is an easier task to solve. Thus, its architecture does not have to be either complex and deep or equipped with mechanisms to deal with various time-series components. To improve the learning performance, we propose weighting individual errors of training samples in the loss function. The error weights correspond to the similarity between the training pattern and the test query pattern. This approach makes the learning process more sensitive to the neighborhood of the test pattern. This means that more distant patterns have less impact on the learned function around the test pattern and lead to improved forecasting accuracy. The proposed framework is useful for a wide range of complex time-series forecasting problems. Its performance is illustrated in several short-term load-forecasting empirical studies in this work. In most cases, error weighting leads to a significant improvement in accuracy. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2021)
Show Figures

Figure 1

Back to TopTop