Topic Editors

Department of Electrical Engineering, Universidad Politecnica de Cartagena, Cartagena, Spain
Department of Applied Mathematics and Statistics, Universidad Politecnica de Cartagena, Cartagena, Spain
Department of Electrical Engineering, Universidad de La Rioja, La Rioja, Spain

Short-Term Load Forecasting

Abstract submission deadline
31 July 2024
Manuscript submission deadline
30 September 2024
Viewed by
17850

Topic 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 systems (planning, scheduling, maintenance, and control processes, among others), and this topic has been an important issue for several decades. However, there is still much progress to be made in this field. The deployment of enabling technologies (e.g., smart meters and sub-metering) has made high granular data available for many customer segments and many tasks—for instance, it has made load forecasting tasks feasible at several demand aggregation levels. The first challenge in this area is the improvement of STLF models and their performance at new demand aggregation levels. Specifically, individual level demand forecasting, which is more challenging than aggregated demand, should be addressed in a comprehensive manner, helping customers in decision making. Moreover, the increasing inclusion of renewable energies (wind and solar power) in the power system, and the necessity of including more flexibility through demand response initiatives, have introduced greater uncertainties, creating new challenges for STLF in future power systems. Other relevant issues are net demand forecasting in “prosumers” (i.e., the integrated or disaggregated forecast of demand and renewable generation), and demand forecasting by end-uses in large or aggregated customers.

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.). In the case of individual loads, the techniques for peak detection and extreme values are of great importance. Furthermore, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new uncertainty sources in the power system has given more importance to probabilistic load forecasting in recent years.

This Topic is concerned with both fundamental research on STLF methodologies and its practical application to power systems, aiming at exploring the challenges that will be faced by a more distributed power system in the future.

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

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

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, etc.)
  • 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
  • renewable generation forecasting
  • short-term net demand forecasting
  • inference on extreme and rare events
  • end-use demand forecasting

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms
2.3 3.7 2008 15 Days CHF 1600 Submit
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600 Submit
Forecasting
forecasting
3.0 4.0 2019 28.5 Days CHF 1800 Submit
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (11 papers)

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27 pages, 10172 KiB  
Article
Combined K-Means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities
by Alex Sleiman and Wencong Su
Energies 2024, 17(6), 1433; https://doi.org/10.3390/en17061433 - 16 Mar 2024
Viewed by 437
Abstract
The power system has undergone significant growth and faced considerable challenges in recent decades, marked by the surge in energy demand and advancements in smart grid technologies, including solar and wind energies, as well as the widespread adoption of electric vehicles. These developments [...] Read more.
The power system has undergone significant growth and faced considerable challenges in recent decades, marked by the surge in energy demand and advancements in smart grid technologies, including solar and wind energies, as well as the widespread adoption of electric vehicles. These developments have introduced a level of complexity for utilities, compounded by the rapid expansion of behind-the-meter (BTM) photovoltaic (PV) systems, each with its own unique design and characteristics, thereby impacting power grid stability and reliability. In response to these intricate challenges, this research focused on the development of a robust forecasting model for load generation. This precision forecasting is crucial for optimal planning, mitigating the adverse effects of PV systems, and reducing operational and maintenance costs. By addressing these key aspects, the goal is to enhance the overall resilience and efficiency of the power grid amidst the evolving landscape of energy and technological advancements. The authors propose a solution leveraging LSTM (long short-term memory) model for a forecasting horizon up to 168 hours. This approach incorporates combinations of K-means clustering, automated meter infrastructure (AMI) real-world PV load generation, weather data, and calculated solar positions to forecast the generation load at customer locations to achieve a 5.7% mean absolute error between the actual and the predicted generation load. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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18 pages, 628 KiB  
Article
Short-Term Load Forecasting Using an LSTM Neural Network for a Grid Operator
by Joan Sebastian Caicedo-Vivas and Wilfredo Alfonso-Morales
Energies 2023, 16(23), 7878; https://doi.org/10.3390/en16237878 - 01 Dec 2023
Viewed by 1183
Abstract
Electricity is crucial for daily life due to the number of activities that depend on it. To forecast future electric load, which changes over time and depends on various factors, grid operators (GOs) must create forecasting models for various time horizons with a [...] Read more.
Electricity is crucial for daily life due to the number of activities that depend on it. To forecast future electric load, which changes over time and depends on various factors, grid operators (GOs) must create forecasting models for various time horizons with a high degree of accuracy because the results have a huge impact on their decision-making regarding, for example, the scheduling of power units to supply user consumption in the short or long term or the installation of new power plants. This has led to the exploration of multiple techniques like statistical models and Artificial Intelligence (AI), with Machine-Learning and Deep-Learning algorithms being the most popular in this latter field. This paper proposes a neural network-based model to forecast short-term load for a Colombian grid operator, considering a seven-day time horizon and using an LSTM recurrent neural network with historical load values from a region in Colombia and calendar features such as holidays and the current month corresponding to the target week. Unlike other LSTM implementations found in the literature, in this work, the LSTM cells read multiple load measurements at once, and the additional information (holidays and current month) is concatenated to the output of the LSTM. The result is used to feed a fully connected neural network to obtain the desired forecast. Due to social problems in the country, the load data presents a strange behavior, which, in principle, affects the prediction capacity of the model. Still, it is eventually able to adjust its forecasts accordingly. The regression metric MAPE measures the model performance, with the best predicted week having an error of 1.65% and the worst week having an error of 26.22%. Additionally, prediction intervals are estimated using bootstrapping. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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17 pages, 3127 KiB  
Article
Short-Term Net Load Forecasting for Regions with Distributed Photovoltaic Systems Based on Feature Reconstruction
by Xudong Zheng, Ming Yang, Yixiao Yu and Chuanqi Wang
Appl. Sci. 2023, 13(16), 9064; https://doi.org/10.3390/app13169064 - 08 Aug 2023
Cited by 2 | Viewed by 799
Abstract
Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integration of distributed photovoltaic systems (DPVS) has caused changes [...] Read more.
Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integration of distributed photovoltaic systems (DPVS) has caused changes in load curve fluctuations. Current deep learning models generally train with historical load series and load-related meteorological data series as input features, which limits the model’s ability to recognize the load fluctuations caused by DPVS. In order to further improve the accuracy of load forecasting models, this paper proposes an input feature reconstruction method based on the maximum information coefficient (MIC). Firstly, the load curves with DPVS are classified by Gaussian mixture model (GMM) clustering. Then, considering the coupling relationship between the load and input features at different times, the load data and input features are reordered. Finally, the MIC between different features and loads at different times is calculated to select the relevant features at those different times and construct new input features. The case analysis shows that the feature reconstruction strategy proposed in this paper effectively improves the prediction performance of deep neural networks. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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22 pages, 3857 KiB  
Article
TS2ARCformer: A Multi-Dimensional Time Series Forecasting Framework for Short-Term Load Prediction
by Songjiang Li, Wenxin Zhang and Peng Wang
Energies 2023, 16(15), 5825; https://doi.org/10.3390/en16155825 - 05 Aug 2023
Cited by 1 | Viewed by 1658
Abstract
Accurately predicting power load is a pressing concern that requires immediate attention. Short-term load prediction plays a crucial role in ensuring the secure operation and analysis of power systems. However, existing research studies have limited capability in extracting the mutual relationships of multivariate [...] Read more.
Accurately predicting power load is a pressing concern that requires immediate attention. Short-term load prediction plays a crucial role in ensuring the secure operation and analysis of power systems. However, existing research studies have limited capability in extracting the mutual relationships of multivariate features in multivariate time series data. To address these limitations, we propose a multi-dimensional time series forecasting framework called TS2ARCformer. The TS2ARCformer framework incorporates the TS2Vec layer for contextual encoding and utilizes the Transformer model for prediction. This combination effectively captures the multi-dimensional features of the data. Additionally, TS2ARCformer introduces a Cross-Dimensional-Self-Attention module, which leverages interactions across channels and temporal dimensions to enhance the extraction of multivariate features. Furthermore, TS2ARCformer leverage a traditional autoregressive component to overcome the issue of deep learning models being insensitive to input scale. This also enhances the model’s ability to extract linear features. Experimental results on two publicly available power load datasets demonstrate significant improvements in prediction accuracy compared to baseline models, with reductions of 43.2% and 37.8% in the aspect of mean absolute percentage error (MAPE) for dataset area1 and area2, respectively. These findings have important implications for the accurate prediction of power load and the optimization of power system operation and analysis. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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16 pages, 10260 KiB  
Article
Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning
by Jiakang Wang, Hui Liu, Guangji Zheng, Ye Li and Shi Yin
Energies 2023, 16(11), 4401; https://doi.org/10.3390/en16114401 - 30 May 2023
Cited by 2 | Viewed by 1040
Abstract
Short-term load forecasting is critical to ensuring the safe and stable operation of the power system. To this end, this study proposes a load power prediction model that utilizes outlier correction, decomposition, and ensemble reinforcement learning. The novelty of this study is as [...] Read more.
Short-term load forecasting is critical to ensuring the safe and stable operation of the power system. To this end, this study proposes a load power prediction model that utilizes outlier correction, decomposition, and ensemble reinforcement learning. The novelty of this study is as follows: firstly, the Hampel identifier (HI) is employed to correct outliers in the original data; secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to extract the waveform characteristics of the data fully; and, finally, the temporal convolutional network, extreme learning machine, and gate recurrent unit are selected as the basic learners for forecasting load power data. An ensemble reinforcement learning algorithm based on Q-learning was adopted to generate optimal ensemble weights, and the predictive results of the three basic learners are combined. The experimental results of the models for three real load power datasets show that: (a) the utilization of HI improves the model’s forecasting result; (b) CEEMDAN is superior to other decomposition algorithms in forecasting performance; and (c) the proposed ensemble method, based on the Q-learning algorithm, outperforms three single models in accuracy, and achieves smaller prediction errors. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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21 pages, 4310 KiB  
Article
Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging
by Raiden Skala, Mohamed Ahmed T. A. Elgalhud, Katarina Grolinger and Syed Mir
Energies 2023, 16(10), 4093; https://doi.org/10.3390/en16104093 - 15 May 2023
Cited by 1 | Viewed by 1205
Abstract
The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and [...] Read more.
The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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20 pages, 5908 KiB  
Article
Point-Interval Forecasting for Electricity Load Based on Regular Fluctuation Component Extraction
by Bilin Shao, Zixuan Yao and Yifan Qiang
Energies 2023, 16(4), 1988; https://doi.org/10.3390/en16041988 - 17 Feb 2023
Cited by 1 | Viewed by 1353
Abstract
The fluctuation and uncertainty of the electricity load bring challenges to load forecasting. Traditional point forecasting struggles to avoid errors, and pure interval forecasting may cause the problem of too wide an interval. In this paper, we combine point forecasting and interval forecasting [...] Read more.
The fluctuation and uncertainty of the electricity load bring challenges to load forecasting. Traditional point forecasting struggles to avoid errors, and pure interval forecasting may cause the problem of too wide an interval. In this paper, we combine point forecasting and interval forecasting and propose a point-interval forecasting model for electricity load based on regular fluctuation component extraction. Firstly, the variational modal decomposition is combined with the sample entropy to decompose the original load series into a strong regular fluctuation component and a weak regular fluctuation component. Then, the gate recurrent unit neural network is used for point forecasting of the strong regular fluctuation component, and the support vector quantile regression model is used for interval forecasting of the weak regular fluctuation component, and the results are accumulated to obtain the final forecasting intervals. Finally, experiments were conducted using electricity load data from two regional electricity grids in Shaanxi Province, China. The results show that combining the idea of point interval, point forecasting, and interval forecasting for components with different fluctuation regularity can effectively reduce the forecasting interval width while having high accuracy. The proposed model has higher forecasting accuracy and smaller mean interval width at various confidence levels compared to the commonly used models. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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30 pages, 4652 KiB  
Article
Forecasting Short-Term Electricity Load Using Validated Ensemble Learning
by Chatum Sankalpa, Somsak Kittipiyakul and Seksan Laitrakun
Energies 2022, 15(22), 8567; https://doi.org/10.3390/en15228567 - 16 Nov 2022
Cited by 5 | Viewed by 1644
Abstract
As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts with weighted averages of forecasts from five [...] Read more.
As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts with weighted averages of forecasts from five individual models: three parametric multiple linear regressors and two non-parametric machine-learning models. The regressors are linear regression models with gradient-descent (LR), ordinary least-squares (OLS) estimators, and generalized least-squares auto-regression (GLSAR) models. In contrast, the machine-learning models are decision trees (DT) and random forests (RF). To select the best model variables and hyper-parameters, we used cross-validation (CV) performance instead of the test data performance, which yielded overly good test performance. We compared various validation schemes and found that the Blocked-CV scheme gives the validation error closest to the test error. Using Blocked-CV, the test results show that the VR model outperforms all its individual predictors. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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22 pages, 4390 KiB  
Article
Automatic Selection of Temperature Variables for Short-Term Load Forecasting
by Alfredo Candela Esclapez, Miguel López García, Sergio Valero Verdú and Carolina Senabre Blanes
Sustainability 2022, 14(20), 13339; https://doi.org/10.3390/su142013339 - 17 Oct 2022
Cited by 2 | Viewed by 1115
Abstract
Due to the infeasibility of large-scale electrical energy storage, electricity is generated and consumed simultaneously. Therefore, electricity entities need consumption forecasting systems to plan operations and manage supplies. In addition, accurate predictions allow renewable energies on electrical grids to be managed, thereby reducing [...] Read more.
Due to the infeasibility of large-scale electrical energy storage, electricity is generated and consumed simultaneously. Therefore, electricity entities need consumption forecasting systems to plan operations and manage supplies. In addition, accurate predictions allow renewable energies on electrical grids to be managed, thereby reducing greenhouse gas emissions. Temperature affects electricity consumption through air conditioning and heating equipment, although it is the consumer’s behavior that determines specifically to what extent. This work proposes an automatic method of processing and selecting variables, with a two-fold objective: improving both the accuracy and the interpretability of the overall forecasting system. The procedure has been tested by the predictive system of the Spanish electricity operator (Red Eléctrica de España) with regard to peninsular demand. During the test period, the forecasting error was consistently reduced for the forecasting horizon, with an improvement of 0.16% in MAPE and 59.71 MWh in RMSE. The new way of working with temperatures is interpretable, since they separate the effect of temperature according to location and time. It has been observed that heat has a greater influence than the cold. In addition, on hot days, the temperature of the second previous day has a greater influence than the previous one, while the opposite occurs on cold days. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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18 pages, 2449 KiB  
Article
A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning
by Yun Duan
Sustainability 2022, 14(14), 8584; https://doi.org/10.3390/su14148584 - 13 Jul 2022
Cited by 6 | Viewed by 1665
Abstract
Energy conservation in buildings has increasingly become a hot issue for the Chinese government. Compared to deterministic load prediction, probabilistic load forecasting is more suitable for long-term planning and management of building energy consumption. In this study, we propose a probabilistic load-forecasting method [...] Read more.
Energy conservation in buildings has increasingly become a hot issue for the Chinese government. Compared to deterministic load prediction, probabilistic load forecasting is more suitable for long-term planning and management of building energy consumption. In this study, we propose a probabilistic load-forecasting method for daily and weekly indoor load. The methodology is based on the long short-term memory (LSTM) model and penalized quantile regression (PQR). A comprehensive analysis for a time period of a year is conducted using the proposed method, and back propagation neural networks (BPNN), support vector machine (SVM), and random forest are applied as reference models. Point prediction as well as interval prediction are adopted to roundly test the prediction performance of the proposed model. Results show that LSTM-PQR has superior performance over the other three models and has improvements ranging from 6.4% to 20.9% for PICP compared with other models. This work indicates that the proposed method fits well with probabilistic load forecasting, which could promise to guide the management of building sustainability in a future carbon neutral scenario. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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19 pages, 7388 KiB  
Article
Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management
by Fan Yu, Lei Wang, Qiaoyong Jiang, Qunmin Yan and Shi Qiao
Energies 2022, 15(12), 4198; https://doi.org/10.3390/en15124198 - 07 Jun 2022
Cited by 4 | Viewed by 1792
Abstract
Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the [...] Read more.
Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the data preprocessing stage, non-parametric kernel density estimation is used to construct customer electricity consumption feature curves, and then historical load data are used to delineate the feasible domain range for outlier detection. In the feature selection stage, the feature data are selected using variational modal decomposition and a maximum information coefficient to enhance the model prediction accuracy. In the model prediction stage, the decomposed intrinsic mode function components are independently predicted and reconstructed using an Informer based on improved self-attention. Additionally, the novel AdaBlief optimizer is used to optimize the model parameters. Cross-sectional and longitudinal experiments are conducted on a regional-level load dataset set in Spain. The experimental results prove that the proposed method is superior to other methods in STLF. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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