Special Issue "Short-Term Load Forecasting 2019"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 January 2020).

Special Issue Editors

Prof. Dr. Antonio Gabaldón
E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad Politecnica de Cartagena, Cartagena, Spain
Tel. 34968338944
Interests: analysis of electrical distribution systems; electricity markets; demand response; energy efficiency; electric haulage in railways and non-invasive monitoring techniques
Prof. 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
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 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 1800 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 (10 papers)

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

Research

Jump to: Review

Open AccessArticle
Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods
Energies 2020, 13(4), 886; https://doi.org/10.3390/en13040886 - 17 Feb 2020
Abstract
An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if the amount of electric load data is insufficient, it is challenging to perform an accurate prediction. To address this issue, we propose a novel electric load forecasting [...] Read more.
An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if the amount of electric load data is insufficient, it is challenging to perform an accurate prediction. To address this issue, we propose a novel electric load forecasting scheme using the electric load data of diverse buildings. We first divide the electric energy consumption data into training and test sets. Then, we construct multivariate random forest (MRF)-based forecasting models according to each building except the target building in the training set and a random forest (RF)-based forecasting model using the limited electric load data of the target building in the test set. In the test set, we compare the electric load of the target building with that of other buildings to select the MRF model that is the most similar to the target building. Then, we predict the electric load of the target building using its input variables via the selected MRF model. We combine the MRF and RF models by considering the different electric load patterns on weekdays and holidays. Experimental results demonstrate that combining the two models can achieve satisfactory prediction performance even if the electric data of only one day are available for the target building. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Open AccessArticle
Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation
Energies 2020, 13(1), 11; https://doi.org/10.3390/en13010011 - 18 Dec 2019
Abstract
The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques [...] Read more.
The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Figure 1

Open AccessArticle
Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting
Energies 2019, 12(22), 4349; https://doi.org/10.3390/en12224349 - 15 Nov 2019
Abstract
With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously [...] Read more.
With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Graphical abstract

Open AccessArticle
A Multi-Step Approach to Modeling the 24-hour Daily Profiles of Electricity Load using Daily Splines
Energies 2019, 12(21), 4169; https://doi.org/10.3390/en12214169 - 01 Nov 2019
Abstract
Forecasting of real-time electricity load has been an important research topic over many years. Electricity load is driven by many factors, including economic conditions and weather. Furthermore, the demand for electricity varies with time, with different hours of the day and different days [...] Read more.
Forecasting of real-time electricity load has been an important research topic over many years. Electricity load is driven by many factors, including economic conditions and weather. Furthermore, the demand for electricity varies with time, with different hours of the day and different days of the week having an effect on the load. This paper proposes a hybrid load-forecasting method that combines classical time series formulations with cubic splines to model electricity load. It is shown that this approach produces a model capable of making short-term forecasts with reasonable accuracy. In contrast to forecasting models that utilize a multitude of regressor variables observed at multiple time points within a day, only the hourly temperature is used in the proposed model and predictive power gains are achieved through the modeling of the 24-hour load profiles across weekends and weekdays while also taking into consideration seasonal variations of such profiles. Long-term trends are accounted for by using population and economic variables. The proposed approach can be used as a stand-alone predictive platform or be used as a scaffolding to build a more complex model involving additional inputs. The data cover the period from 1 January 1993 through 31 December 2013 from the Atlantic City Electric zone. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Graphical abstract

Open AccessArticle
Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation
Energies 2019, 12(18), 3560; https://doi.org/10.3390/en12183560 - 17 Sep 2019
Abstract
Advanced metering infrastructure (AMI) is spreading to households in some countries, and could be a source for forecasting the residential electric demand. However, load forecasting of a single household is still a fairly challenging topic because of the high volatility and uncertainty of [...] Read more.
Advanced metering infrastructure (AMI) is spreading to households in some countries, and could be a source for forecasting the residential electric demand. However, load forecasting of a single household is still a fairly challenging topic because of the high volatility and uncertainty of the electric demand of households. Moreover, there is a limitation in the use of historical load data because of a change in house ownership, change in lifestyle, integration of new electric devices, and so on. The paper proposes a novel method to forecast the electricity loads of single residential households. The proposed forecasting method is based on convolution neural networks (CNNs) combined with a data-augmentation technique, which can artificially enlarge the training data. This method can address issues caused by a lack of historical data and improve the accuracy of residential load forecasting. Simulation results illustrate the validation and efficacy of the proposed method. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Figure 1

Open AccessArticle
Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System
Energies 2019, 12(17), 3359; https://doi.org/10.3390/en12173359 - 30 Aug 2019
Abstract
A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory [...] Read more.
A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Graphical abstract

Open AccessArticle
Short-Term Electricity Demand Forecasting Using Components Estimation Technique
Energies 2019, 12(13), 2532; https://doi.org/10.3390/en12132532 - 01 Jul 2019
Cited by 1
Abstract
Currently, in most countries, the electricity sector is liberalized, and electricity is traded in deregulated electricity markets. In these markets, electricity demand is determined the day before the physical delivery through (semi-)hourly concurrent auctions. Hence, accurate forecasts are essential for efficient and effective [...] Read more.
Currently, in most countries, the electricity sector is liberalized, and electricity is traded in deregulated electricity markets. In these markets, electricity demand is determined the day before the physical delivery through (semi-)hourly concurrent auctions. Hence, accurate forecasts are essential for efficient and effective management of power systems. The electricity demand and prices, however, exhibit specific features, including non-constant mean and variance, calendar effects, multiple periodicities, high volatility, jumps, and so on, which complicate the forecasting problem. In this work, we compare different modeling techniques able to capture the specific dynamics of the demand time series. To this end, the electricity demand time series is divided into two major components: deterministic and stochastic. Both components are estimated using different regression and time series methods with parametric and nonparametric estimation techniques. Specifically, we use linear regression-based models (local polynomial regression models based on different types of kernel functions; tri-cubic, Gaussian, and Epanechnikov), spline function-based models (smoothing splines, regression splines), and traditional time series models (autoregressive moving average, nonparametric autoregressive, and vector autoregressive). Within the deterministic part, special attention is paid to the estimation of the yearly cycle as it was previously ignored by many authors. This work considers electricity demand data from the Nordic electricity market for the period covering 1 January 2013–31 December 2016. To assess the one-day-ahead out-of-sample forecasting accuracy, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are calculated. The results suggest that the proposed component-wise estimation method is extremely effective at forecasting electricity demand. Further, vector autoregressive modeling combined with spline function-based regression gives superior performance compared with the rest. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Figure 1

Open AccessArticle
Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting
Energies 2019, 12(10), 1931; https://doi.org/10.3390/en12101931 - 20 May 2019
Cited by 3
Abstract
Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are [...] Read more.
Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Figure 1

Open AccessArticle
Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study
Energies 2019, 12(7), 1253; https://doi.org/10.3390/en12071253 - 01 Apr 2019
Cited by 3
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Figure 1

Review

Jump to: Research

Open AccessReview
Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview
Energies 2019, 12(3), 393; https://doi.org/10.3390/en12030393 - 27 Jan 2019
Cited by 8
Abstract
Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility [...] Read more.
Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Show Figures

Figure 1

Back to TopTop