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Machine Learning-Based Energy Forecasting and Its Applications

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 20547

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Special Issue Editors

Prof. Dr. Yungcheol Byun

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Guest Editor

Department of Computer Engineering, Jeju National University, Jeju-si 63243, Republic of Korea
Interests: AI and machine learning; pattern recognition; sensor; knowledge discovery; time-series data analysis and prediction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jeong-Do Park

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Division of Energy & Electrical Engineering, Uiduk University, Gyeongju 38004, Korea
Interests: electric power system operation; load forecasting; state estimation; economic dispatch and unit commitment

Prof. Dr. Neungsoo Park

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Guest Editor

Division of Computer Science & Engineering, Konkuk University, Seoul 05029, Korea
Interests: machine learning; forecasting of PV power generations; time series data analysis; neural networks and deep learing; GPGPU computing

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to present a forum for researchers comprising the entire range of artificial intelligence and machine-learning-based applications in the energy sector.

The world’s energy sector is facing growing challenges, such as a demand and efficiency increase, supply and demand pattern change, and the absence of a best management analysis. In developing countries, this challenge is even more intense. Transferring data of the energy sector to artificial intelligence can gradually solve this problem. Artificial intelligence algorithms can analyze equipment data, solve problems, and then save money, time, and life. Artificial intelligence applications are also used for smart energy consumption. Modern homes with machine learning algorithms can automatically respond to fluctuations in electricity prices and control energy usage. Systems based on machine learning can help energy suppliers to prepare to keep pace with fluctuating renewable energy supplies. To reduce interest in low-emission energy and oil dependence, solar PV, wind farms, and marine energy systems increase their installed capacity worldwide. Artificial intelligence algorithms can continuously improve monitoring, analyze energy consumption, discover new problems, and perform analysis to improve performance. Price optimization models use the power of neural networks to predict energy demand and create improved price recommendations to help energy companies to achieve their goals. Hence, artificial intelligence and machine learning can play a crucial role in practically managing the challenges of the energy sector.

In this Special Issue, we would like to encourage people to contribute their latest developments and ideas and review articles on machine-learning-based energy forecasting and its applications. This Special Issue will focus on essential AI-based applications in the energy sector. However, it is not limited to the following:

Supply and demand patterns variations;
Management analysis of energy sector;
Optimization of renewable energy using machine learning;
Forecasting model for wind speed and solar radiations;
AI to overwhelm future energy problems;
Fluctuations in electricity prices and control energy usage;
Predictive models for smart grids;
Forecasting of PV power generation;
Electricity market price prediction using advanced deep learning;
Ensemble forecasting models;
Reinforcement learning and predictive control for smart energy systems;
Data mining applications in understanding electricity consumers;
Hybrid and combined models.

We encourage you to submit your original work to this issue and look forward to receiving your distinguished research.

Prof. Dr. Yungcheol Byun
Prof. Dr. Jeong-Do Park
Prof. Dr. Neungsoo Park
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 submissions that pass pre-check are 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 2600 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

artificial intelligence and machine learning in energy
forecasting energy consumption
energy informatics
renewable energy generation and prediction
deep learning and renewable energy
time series forecasting
wind, solar, and wave energy
electricity price forecasting using machine learning
PV system and machine learning
energy feature engineering
energy and time series data analysis
ensemble model for energy
machine learning and its applications for energy
big data and machine learning for energy
predictive analytics
machine-learning-based sustainability in energy
deep neural networks and regression analysis for energy

Published Papers (7 papers)

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Research

22 pages, 2823 KiB

Open AccessArticle

Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

by Prince Waqas Khan, Yongjun Kim, Yung-Cheol Byun and Sang-Joon Lee

Energies 2021, 14(21), 7167; https://doi.org/10.3390/en14217167 - 01 Nov 2021

Cited by 16 | Viewed by 2576

Abstract

Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning-based methods to improve the prediction accuracy of energy consumption. However, the discrepancy between actual energy consumption and predicted energy consumption is still challenging. Various factors, including changes in weather, holidays, and weekends, affect prediction accuracy. This article analyses the overall prediction using error curve learning and a hybrid model. Actual energy consumption data of Jeju island, South Korea, has been used for experimental purposes. We have used a hybrid ML model consisting of Catboost, Xgboost, and Multi-layer perceptron for the prediction. Then we analyze the factors that affect the week-ahead (WA) and 48 h prediction results. Mean error on weekdays is recorded as 2.78%, for weekends 2.79%, and for special days it is recorded as 4.28%. We took into consideration significant predicting errors and looked into the reasons behind those errors. Furthermore, we analyzed whether factors, such as a sudden change in temperature and typhoons, had an effect on energy consumption. Finally, the authors have considered the other factors, such as public holidays and weekends, to analyze the significant errors in the prediction. This study can be helpful for policymakers to make policies according to the error-causing factors. Full article

(This article belongs to the Special Issue Machine Learning-Based Energy Forecasting and Its Applications)

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19 pages, 24101 KiB

Open AccessArticle

A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality

by Dimitrios Kontogiannis, Dimitrios Bargiotas, Aspassia Daskalopulu and Lefteri H. Tsoukalas

Energies 2021, 14(19), 6088; https://doi.org/10.3390/en14196088 - 24 Sep 2021

Cited by 4 | Viewed by 1464

Abstract

Power forecasting models offer valuable insights on the electricity consumption patterns of clients, enabling the development of advanced strategies and applications aimed at energy saving, increased energy efficiency, and smart energy pricing. The data collection process for client consumption models is not always ideal and the resulting datasets often lead to compromises in the implementation of forecasting models, as well as suboptimal performance, due to several challenges. Therefore, combinations of elements that highlight relationships between clients need to be investigated in order to achieve more accurate consumption predictions. In this study, we exploited the combined effects of client similarity and causality, and developed a power consumption forecasting model that utilizes ensembles of long short-term memory (LSTM) networks. Our novel approach enables the derivation of different representations of the predicted consumption based on feature sets influenced by similarity and causality metrics. The resulting representations were used to train a meta-model, based on a multi-layer perceptron (MLP), in order to combine the results of the LSTM ensembles optimally. This combinatorial approach achieved better overall performance and yielded lower mean absolute percentage error when compared to the standalone LSTM ensembles that do not include similarity and causality. Additional experiments indicated that the combination of similarity and causality resulted in more performant models when compared to implementations utilizing only one element on the same model structure. Full article

(This article belongs to the Special Issue Machine Learning-Based Energy Forecasting and Its Applications)

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20 pages, 9144 KiB

Open AccessArticle

MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

by Kyung-Sang Ryu, Dae-Jin Kim, Heesang Ko, Chang-Jin Boo, Jongrae Kim, Young-Gyu Jin and Ho-Chan Kim

Energies 2021, 14(13), 4041; https://doi.org/10.3390/en14134041 - 04 Jul 2021

Cited by 17 | Viewed by 2827

Abstract

This paper presents the improvements of the hosting capacity of photovoltaics (PVs) and electric vehicles (EVs) in a stand-alone microgrid (MG) with an energy storage system (ESS) by consider-ing a model predictive control (MPC) based energy management system. The system is configured as an MG, including PVs, an ESS, a diesel generator (DG), and several loads with EVs. The DG is controlled to operate at rated power and the MPC algorithm is used in a stand-alone MG, which supplies the energy demanded for several loads with EVs. The hosting capacity of the load in-cluding the EV and PVs can be expanded through the ESS to the terminal node of the microgrid. In this case, the PVs and the load can be connected in excess of the capacity of the diesel genera-tor, and each bus in the feeder complies with the voltage range required by the grid. The effec-tiveness of the proposed algorithm to resolve the hosting capacity is demonstrated by numerical simulations. Full article

(This article belongs to the Special Issue Machine Learning-Based Energy Forecasting and Its Applications)

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25 pages, 1683 KiB

Open AccessArticle

An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

by Anam-Nawaz Khan, Naeem Iqbal, Atif Rizwan, Rashid Ahmad and Do-Hyeun Kim

Energies 2021, 14(11), 3020; https://doi.org/10.3390/en14113020 - 23 May 2021

Cited by 41 | Viewed by 3785

Abstract

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting. Full article

(This article belongs to the Special Issue Machine Learning-Based Energy Forecasting and Its Applications)

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18 pages, 2013 KiB

Open AccessArticle

Methods for Integrating Extraterrestrial Radiation into Neural Network Models for Day-Ahead PV Generation Forecasting

by Seung Chan Jo, Young Gyu Jin, Yong Tae Yoon and Ho Chan Kim

Energies 2021, 14(9), 2601; https://doi.org/10.3390/en14092601 - 01 May 2021

Cited by 2 | Viewed by 1703

Abstract

Variability, intermittency, and limited controllability are inherent characteristics of photovoltaic (PV) generation that result in inaccurate solutions to scheduling problems and the instability of the power grid. As the penetration level of PV generation increases, it becomes more important to mitigate these problems by improving forecasting accuracy. One of the alternatives to improving forecasting performance is to include a seasonal component. Thus, this study proposes using information on extraterrestrial radiation (ETR), which is the solar radiation outside of the atmosphere, in neural network models for day-ahead PV generation forecasting. Specifically, five methods for integrating the ETR into the neural network models are presented: (1) division preprocessing, (2) multiplication preprocessing, (3) replacement of existing input, (4) inclusion as additional input, and (5) inclusion as an intermediate target. The methods were tested using two datasets in Australia using four neural network models: Multilayer perceptron and three recurrent neural network(RNN)-based models including vanilla RNN, long short-term memory, and gated recurrent unit. It was found that, among the integration methods, including the ETR as the intermediate target improved the mean squared error by 4.1% on average, and by 12.28% at most in RNN-based models. These results verify that the integration of ETR into the PV forecasting models based on neural networks can improve the forecasting performance. Full article

(This article belongs to the Special Issue Machine Learning-Based Energy Forecasting and Its Applications)

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17 pages, 1294 KiB

Open AccessArticle

Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

by Hyung Keun Ahn and Neungsoo Park

Energies 2021, 14(2), 436; https://doi.org/10.3390/en14020436 - 15 Jan 2021

Cited by 37 | Viewed by 3455

Abstract

Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R²-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R²-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy. Full article

(This article belongs to the Special Issue Machine Learning-Based Energy Forecasting and Its Applications)

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14 pages, 3438 KiB

Open AccessArticle

Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network

by Weizhen Wang, Jiaojiao Feng and Feinan Xu

Energies 2021, 14(2), 273; https://doi.org/10.3390/en14020273 - 06 Jan 2021

Cited by 6 | Viewed by 1945

Abstract

Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth’s surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg–Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER). The DSSR observations from the four-component radiation sensor installed at the land covered by vegetable, village, maize, orchard, Gobi, sandy desert, desert steppe, and wetland were used to validate the model estimates. The results showed that the estimates of DSSR from LM-BP agreed well with the site measurements, with the root mean square error (RMSE) and the mean bias error (MBE) values of 27.34 W/m² and −1.59 W/m², respectively. This indicates that by combining the LM-BP network model and ASTER images can obtain precise DSSR in heterogenous surface. The DSSR results of this study can provide accurate high-spatial resolution input data for hydrological, evapotranspiration, and crop models. Full article

(This article belongs to the Special Issue Machine Learning-Based Energy Forecasting and Its Applications)

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