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The Energy Consumption and Load Forecasting Challenges

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

Deadline for manuscript submissions: closed (22 June 2023) | Viewed by 43150

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Prof. Dr. Filipe Rodrigues

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1. Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro, 1959-007 Lisboa, Portugal
2. IDMEC–Instituto de Engenharia Mecânica, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Interests: energy application; sustainable energy; renewable energy; energy demand; load forecasting; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. João M. F. Calado

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IDMEC/ISEL - Instituto Superior de Engenharia de Lisboa, Departamento de Engenharia Mecânica, Instituto Politécnico de Lisboa, 1500-310 Lisboa, Portugal
Interests: fault detection and isolation; intelligent automatic control; collaborative systems; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy is essential for sustainable development in any country, so be it social, economic or environmental. Environment emissions can be diminished through decreased dependence on fossil fuels, the placement of renewable energy generation sources and electricity consumption management. The dependency on fossil fuel, the negative impact on the environment and price volatility have encouraged and increase the energy efficiency activities and changes the electric power systems. One of those changes has been the deployment of the decentralized, local and microgeneration from renewable energy sources (RES). Although, smart grids (SG) solve many of the contemporary problems it gives rise to new control and resolves issues especially with the growing role of RES. The other change has been the energy market liberalization has brought greater competitiveness to the sector and the growing use of RES have also brought greater complexity to the energy transition process. Both, liberalization, and energy transition complexity can be addressed through the digitization and integration of the energy system to create an Internet of Energy (IoE). To take full advantage of the opportunities provided by digitization, it will be required to ensure both a coordinated cooperation between all the main shakeholders and rigorous monitoring systems. The digitalization and the potential provided by smart networking, as SG, Internet-of-Thing or Internet-of-People, provide the opportunity to face these challenges and will, therefore, represent an important turning point for the future energy systems. During the last decade, several news techniques are being used for energy demand management to accurately forecast the future energy needs. Modeling systems are being used increasingly to predict energy and prices. For a prosperous future, a sustainable economic and secure environment it is crucial an energy management that demand a proper allocation of available resources.

This Special Issue aims to group all the alternative paradigms that are being developed to go beyond the current energy forecasting modeling systems and prices. These include, but not exclusively:

Energy and load Forecasting
Demand-side management
Heuristic techniques;
Optimization algorithms;
Neural networks and Deep learning;
Support vector machine;
Fuzzy systems and genetic algorithm;
Hybrid methods with AI.

Prof. Filipe Rodrigues
Prof. João M. F. Calado
Guest Editors

Manuscript Submission Information

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Keywords

Energy demand
renewable energy
internet of energy
Industry 4.0
Artificial Intelligence

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Published Papers (12 papers)

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Research

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14 pages, 4170 KiB

Open AccessArticle

Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

by Shafqat Jawad and Junyong Liu

Energies 2023, 16(13), 5178; https://doi.org/10.3390/en16135178 - 5 Jul 2023

Cited by 6 | Viewed by 1404

Abstract

Charging load mobility evaluation becomes one of the main concerns for charging services and power system stability due to the stochastic nature of electrical vehicles (EVs) and is critical for the robust scheduling of economic operations at different intervals. Therefore, the EV spatial–temporal approach for load mobility forecasting is presented in this article. Furthermore, the reliability indicators of large-scale EV distribution network penetration are analyzed. The Markov decision process (MDP) theory and Monte Carlo simulation are applied to efficiently forecast the charging load and stochastic path planning. A spatial–temporal model is established to robustly forecast the load demand, stochastic path planning, traffic conditions, and temperatures under different scenarios to evaluate the charging load mobility and EV drivers’ behavior. In addition, the distribution network performance indicators are explicitly evaluated. A Monte Carlo simulation is adopted to examine system stability considering various charging scenarios. Urban coupled traffic-distribution networks comprising 30-node transportation and 33-bus distribution networks are considered as a test case to illustrate the proposed study. The results analysis reveals that the proposed method can robustly estimate the charging load mobility. Furthermore, significant EV penetrations, weather, and traffic congestion further adversely affect the performance of the power system. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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20 pages, 2182 KiB

Open AccessArticle

A Reinforcement Learning Approach for Ensemble Machine Learning Models in Peak Electricity Forecasting

by Warut Pannakkong, Vu Thanh Vinh, Nguyen Ngoc Minh Tuyen and Jirachai Buddhakulsomsiri

Energies 2023, 16(13), 5099; https://doi.org/10.3390/en16135099 - 1 Jul 2023

Cited by 5 | Viewed by 2151

Abstract

Electricity peak load forecasting plays an important role in electricity generation capacity planning to ensure reliable power supplies. To achieve high forecast accuracy, multiple machine learning models have been implemented to forecast the monthly peak load in Thailand over the past few years, yielding promising results. One approach to further improve forecast accuracy is to effectively select the most accurate forecast value for each period from among the forecast values generated by these models. This article presents a novel reinforcement learning approach using the double deep Q-network (Double DQN), which acts as a model selector from a pool of available models. The monthly electricity peak load data of Thailand from 2004 to 2017 are used to demonstrate the effectiveness of the proposed method. A hyperparameter tuning methodology using a fractional factorial design is implemented to significantly reduce the number of required experimental runs. The results indicate that the proposed selection model using Double DQN outperforms all tested individual machine learning models in terms of mean square error. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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20 pages, 3646 KiB

Open AccessArticle

Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network

by George Kandilogiannakis, Paris Mastorocostas, Athanasios Voulodimos and Constantinos Hilas

Energies 2023, 16(10), 4227; https://doi.org/10.3390/en16104227 - 20 May 2023

Cited by 4 | Viewed by 1728

Abstract

A dynamic fuzzy neural network for short-term load forecasting of the Greek power system is proposed, and an hourly based prediction for the whole year is performed. A DBD-FELF (Dynamic Block-Diagonal Fuzzy Electric Load Forecaster) consists of fuzzy rules with consequent parts that are neural networks with internal recurrence. These networks have a hidden layer, which consists of pairs of neurons with feedback connections between them. The overall fuzzy model partitions the input space in partially overlapping fuzzy regions, where the recurrent neural networks of the respective rules operate. The partition of the input space and determination of the fuzzy rule base is performed via the use of the Fuzzy C-Means clustering algorithm, and the RENNCOM constrained optimization method is applied for consequent parameter tuning. The performance of DBD-FELF is tested via extensive experimental analysis, and the results are promising, since an average percentage error of 1.18% is attained, along with an average yearly absolute error of 76.2 MW. Moreover, DBD-FELF is compared with Deep Learning, fuzzy and neurofuzzy rivals, such that its particular attributes are highlighted. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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21 pages, 6159 KiB

Open AccessArticle

Energy Consumption Forecasting in a University Office by Artificial Intelligence Techniques: An Analysis of the Exogenous Data Effect on the Modeling

by Roozbeh Sadeghian Broujeny, Safa Ben Ayed and Mouadh Matalah

Energies 2023, 16(10), 4065; https://doi.org/10.3390/en16104065 - 12 May 2023

Cited by 8 | Viewed by 2004

Abstract

The forecasting of building energy consumption remains a challenging task because of the intricate management of the relevant parameters that can influence the performance of models. Due to the powerful capability of artificial intelligence (AI) in forecasting problems, it is deemed to be highly effective in this domain. However, achieving accurate predictions requires the extraction of meaningful historical knowledge from various features. Given that the exogenous data may affect the energy consumption forecasting model’s accuracy, we propose an approach to study the importance of data and selecting optimum time lags to obtain a high-performance machine learning-based model, while reducing its complexity. Regarding energy consumption forecasting, multilayer perceptron-based nonlinear autoregressive with exogenous inputs (NARX), long short-term memory (LSTM), gated recurrent unit (GRU), decision tree, and XGboost models are utilized. The best model performance is achieved by LSTM and GRU with a root mean square error of 0.23. An analysis by the Diebold–Mariano method is also presented, to compare the prediction accuracy of the models. In order to measure the association of feature data on modeling, the “model reliance” method is implemented. The proposed approach shows promising results to obtain a well-performing model. The obtained results are qualitatively reported and discussed. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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24 pages, 4975 KiB

Open AccessArticle

A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting

by Lalitpat Aswanuwath, Warut Pannakkong, Jirachai Buddhakulsomsiri, Jessada Karnjana and Van-Nam Huynh

Energies 2023, 16(4), 1860; https://doi.org/10.3390/en16041860 - 13 Feb 2023

Cited by 11 | Viewed by 2899

Abstract

Daily electricity peak load forecasting is important for electricity generation capacity planning. Accurate forecasting leads to saving on excessive electricity generating capacity, while maintaining the stability of the power system. The main challenging tasks in this research field include improving forecasting accuracy and reducing computational time. This paper proposes a hybrid model involving variational mode decomposition (VMD), empirical mode decomposition (EMD), fast Fourier transform (FFT), stepwise regression, similar days selection (SD) method, and artificial neural network (ANN) for daily electricity peak load forecasting. Stepwise regression and similar days selection method are used for input variable selection. VMD and FFT are applied for data decomposition and seasonality capturing, while EMD is employed for determining an appropriate decomposition level for VMD. The hybrid model is constructed to effectively forecast special holidays, which have different patterns from other normal weekdays and weekends. The performance of the hybrid model is tested with real electricity peak load data provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Experimental results show that the hybrid model gives the best performance while saving computation time by solving the problems in input variable selection, data decomposition, and imbalance data of normal and special days in the training process. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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23 pages, 2875 KiB

Open AccessArticle

Transformer-Based Model for Electrical Load Forecasting

by Alexandra L’Heureux, Katarina Grolinger and Miriam A. M. Capretz

Energies 2022, 15(14), 4993; https://doi.org/10.3390/en15144993 - 8 Jul 2022

Cited by 77 | Viewed by 8390

Abstract

Amongst energy-related CO

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Amongst energy-related CO

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emissions, electricity is the largest single contributor, and with the proliferation of electric vehicles and other developments, energy use is expected to increase. Load forecasting is essential for combating these issues as it balances demand and production and contributes to energy management. Current state-of-the-art solutions such as recurrent neural networks (RNNs) and sequence-to-sequence algorithms (Seq2Seq) are highly accurate, but most studies examine them on a single data stream. On the other hand, in natural language processing (NLP), transformer architecture has become the dominant technique, outperforming RNN and Seq2Seq algorithms while also allowing parallelization. Consequently, this paper proposes a transformer-based architecture for load forecasting by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features. Moreover, in contrast to most load forecasting studies, we evaluate the proposed solution on different data streams under various forecasting horizons and input window lengths in order to ensure result reproducibility. Results show that the proposed approach successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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21 pages, 1712 KiB

Open AccessArticle

Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models

by Warut Pannakkong, Thanyaporn Harncharnchai and Jirachai Buddhakulsomsiri

Energies 2022, 15(9), 3105; https://doi.org/10.3390/en15093105 - 24 Apr 2022

Cited by 23 | Viewed by 3348

Abstract

This article involves forecasting daily electricity consumption in Thailand. Electricity consumption data are provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Five forecasting techniques, including multiple linear regression, artificial neural network (ANN), support vector machine, hybrid models, and ensemble models, are implemented. The article proposes a hyperparameter tuning technique, called sequential grid search, which is based on the widely used grid search, for ANN and hybrid models. Auxiliary variables and indicator variables that can improve the models’ forecasting performance are included. From the computational experiment, the hybrid model of a multiple regression model to forecast the expected daily consumption and ANNs from the sequential grid search to forecast the error term, along with additional indicator variables for some national holidays, provides the best mean absolution percentage error of 1.5664% on the test data set. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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19 pages, 3674 KiB

Open AccessArticle

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

by Jintae Cho, Yeunggul Yoon, Yongju Son, Hongjoo Kim, Hosung Ryu and Gilsoo Jang

Energies 2022, 15(9), 2987; https://doi.org/10.3390/en15092987 - 19 Apr 2022

Cited by 11 | Viewed by 2939

Abstract

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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22 pages, 6051 KiB

Open AccessArticle

Home Energy Forecast Performance Tool for Smart Living Services Suppliers under an Energy 4.0 and CPS Framework

by Filipe Martins Rodrigues, Carlos Cardeira, João M. F. Calado and Rui Melicio

Energies 2022, 15(3), 957; https://doi.org/10.3390/en15030957 - 28 Jan 2022

Cited by 3 | Viewed by 2763

Abstract

Industry 4.0 is a paradigm consisting of cyber-physical systems based on the interconnection between all sorts of machines, sensors, and actuators, generally known as things. The combination of energy technology and information and technology communication (ICT) enables measurement, control, and automation to be performed across the distributed grid with high time resolution. Through digital revolution in the energy sector, the term Energy 4.0 emerges in the future electric sector. The growth outlook for appliance usage is increasing and the appearance of renewable energy sources on the electric grid requires strategies to control demand and peak loads. Potential feedback for energy performance is the use of smart meters in conjunction with smart energy management; well-designed applications will successfully inform, engage, empower, and motivate consumers. This paper presents several hands-on tools for load forecasting, comparing previous works and verifying which show the best energy forecasting performance in a smart monitoring system. Simulations were performed based on forecasting of the hours ahead of the load for several households. Special attention was given to the accuracy of the forecasting model for weekdays and weekends. The development of the proposed methods, based on artificial neural networks (ANN), provides more reliable forecasting for a few hours ahead and peak loads. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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24 pages, 12840 KiB

Open AccessFeature PaperArticle

Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

by Andrea Maria N. C. Ribeiro, Pedro Rafael X. do Carmo, Patricia Takako Endo, Pierangelo Rosati and Theo Lynn

Energies 2022, 15(3), 750; https://doi.org/10.3390/en15030750 - 20 Jan 2022

Cited by 38 | Viewed by 4533

Abstract

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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21 pages, 1338 KiB

Open AccessArticle

Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting

by Pedro M. R. Bento, Jose A. N. Pombo, Maria R. A. Calado and Silvio J. P. S. Mariano

Energies 2021, 14(21), 7378; https://doi.org/10.3390/en14217378 - 5 Nov 2021

Cited by 35 | Viewed by 4043

Abstract

Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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Review

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26 pages, 2167 KiB

Open AccessReview

Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review

by Filipe Rodrigues, Carlos Cardeira, João M. F. Calado and Rui Melicio

Energies 2023, 16(10), 4098; https://doi.org/10.3390/en16104098 - 15 May 2023

Cited by 18 | Viewed by 3153

Abstract

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. Full article

(This article belongs to the Special Issue The Energy Consumption and Load Forecasting Challenges)

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