Energies

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19 pages, 4301 KiB

Open AccessArticle

Imitation Learning with Deep Attentive Tabular Neural Networks for Environmental Prediction and Control in Smart Home

by Omar al-Ani, Sanjoy Das and Hongyu Wu

Energies 2023, 16(13), 5091; https://doi.org/10.3390/en16135091 - 30 Jun 2023

Cited by 5 | Viewed by 1767

Automated indoor environmental control is a research topic that is beginning to receive much attention in smart home automation. All machine learning models proposed to date for this purpose have relied on reinforcement learning using simple metrics of comfort as reward signals. Unfortunately, [...] Read more.

Automated indoor environmental control is a research topic that is beginning to receive much attention in smart home automation. All machine learning models proposed to date for this purpose have relied on reinforcement learning using simple metrics of comfort as reward signals. Unfortunately, such indicators do not take into account individual preferences and other elements of human perception. This research explores an alternative (albeit closely related) paradigm called imitation learning. In the proposed architecture, machine learning models are trained with tabular data pertaining to environmental control activities of the real occupants of a residential unit. This eliminates the need for metrics that explicitly quantify human perception of comfort. Moreover, this article introduces the recently proposed deep attentive tabular neural network (TabNet) into smart home research by incorporating TabNet-based components within its overall framework. TabNet has consistently outperformed all other popular machine learning models in a variety of other application domains, including gradient boosting, which was previously considered ideal for learning from tabular data. The results obtained herein strongly suggest that TabNet is the best choice for smart home applications. Simulations conducted using the proposed architecture demonstrate its effectiveness in reproducing the activity patterns of the home unit’s actual occupants. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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20 pages, 5706 KiB

Open AccessArticle

Detection and Diagnosis of Multiple-Dependent Faults (MDFDD) of Water-Cooled Centrifugal Chillers Using Grey-Box Model-Based Method

by Hongwen Dou and Radu Zmeureanu

Energies 2023, 16(1), 210; https://doi.org/10.3390/en16010210 - 25 Dec 2022

Cited by 7 | Viewed by 1662

Abstract

This paper presents the development and use of benchmarking grey-box models for the detection and diagnosis of multiple-dependent faults (MDFDD) of a water-cooled centrifugal chiller. Models are developed using data recorded by a Building Automation System (BAS) from a central cooling plant of [...] Read more.

This paper presents the development and use of benchmarking grey-box models for the detection and diagnosis of multiple-dependent faults (MDFDD) of a water-cooled centrifugal chiller. Models are developed using data recorded by a Building Automation System (BAS) from a central cooling plant of an institutional building. The forward residual-based fault detection model identifies a fault symptom, when the difference between the measured value of target variable and benchmarking value exceeds the corresponding threshold. For the fault diagnosis, most publications start from a known single fault and establish the impact on following variables in the system. This paper presents a rule-based backward approach. The proposed method identifies if (i) the fault symptom is correct (i.e., a variable has abnormal values), or (ii) the fault symptom is incorrect (i.e., the symptom of target variable is caused by impacts generated by other faulty variables due to the dependency between variables), or (iii) both target and regressor variables are abnormal. For testing the proposed MDFDD model, some artificial faults are inserted into the measurement data file, and results are discussed about the method potential for the application. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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29 pages, 1942 KiB

Open AccessArticle

Machine Learning Predictions of Electricity Capacity

by Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart and Martin Zwick

Energies 2023, 16(1), 187; https://doi.org/10.3390/en16010187 - 24 Dec 2022

Cited by 2 | Viewed by 2016

Abstract

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and [...] Read more.

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be reduced by over 25% with the margin of error currently used in the industry while also significantly improving closeness and exceedance metrics. The reduction in INC and DEC capacity requirements would yield an approximate cost savings of $4 million annually for one of nineteen Western Energy Imbalance market participants. Reconstructability Analysis performs the best among the machine learning methods tested. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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24 pages, 4267 KiB

Open AccessArticle

Application of Bayesian Networks in Modeling of Underground Gas Storage Energy Security

by Piotr Kosowski, Katarzyna Kosowska and Wojciech Nawalaniec

Energies 2022, 15(14), 5185; https://doi.org/10.3390/en15145185 - 18 Jul 2022

Cited by 3 | Viewed by 2278

Abstract

Energy security is a multidimensional and multifaceted concept, therefore defining it is a complex problem. It requires the consideration of a wide set of factors from the fields of economics, geology, ecology and geopolitics, all of which have an influence on energy security [...] Read more.

Energy security is a multidimensional and multifaceted concept, therefore defining it is a complex problem. It requires the consideration of a wide set of factors from the fields of economics, geology, ecology and geopolitics, all of which have an influence on energy security or the lack thereof. The article focuses on natural gas, which is a very specific fuel in the European context. It is the most “politicized” source of energy, as a consequence of its growing importance as a transition fuel in the energy transformation process. In order to identify dependencies between variables on the gas market and analyze their impact on it (in particular on underground storage), the authors chose a set of variables and built a Bayesian network. The network is an effective and flexible tool that allows analysis of the relationships between the variables that build them and model their values based on evidence. The article presents two stages of work with the Bayesian network. In the first one, a network was built based on historical data. It shows the relationships between the variables as well as the probability of the value ranges of individual variables. A huge advantage of the presented Bayesian network is that it can be used to model various scenarios on the gas market. Moreover, the ability to make statistical inferences for all its nodes represents a valuable additional feature. Several examples of such inferences are presented in the second stage of the analysis, examining the impact of consumption variability on the level of inventory in underground gas storage facilities, the impact of having an LNG terminal and the share of natural gas in electricity production on the storage capacity of a given country. The use of tools such as Bayesian networks allows us to better discover the interrelationships between variables influencing the energy market, analyze them, and estimate the impact on energy security of distinct scenarios described with specific metrics. A simple example of such a metric, i.e., the minimum level of gas storage at the end of the winter season, as well as its analysis and modeling using a relatively simple Bayesian network, is presented in this article. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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24 pages, 52531 KiB

Open AccessFeature PaperArticle

Using Spatial Data Science in Energy-Related Modeling of Terraforming the Martian Atmosphere

by Piotr Pałka, Robert Olszewski and Agnieszka Wendland

Energies 2022, 15(14), 4957; https://doi.org/10.3390/en15144957 - 6 Jul 2022

Cited by 1 | Viewed by 2733

Abstract

This paper proposes a methodology for numerical modeling of terraforming Mars’ atmosphere using high-energy asteroid impact and greenhouse gas production processes. The developed simulation model uses a spatial data science approach to analyze the Global Climate Model of Mars and cellular automata to [...] Read more.

This paper proposes a methodology for numerical modeling of terraforming Mars’ atmosphere using high-energy asteroid impact and greenhouse gas production processes. The developed simulation model uses a spatial data science approach to analyze the Global Climate Model of Mars and cellular automata to model the changes in Mars’ atmospheric parameters. The developed model allows estimating the energy required to raise the planet’s temperature by sixty degrees using different variations of the terraforming process. Using a data science approach for spatial big data analysis has enabled successful numerical simulations of global and local atmospheric changes on Mars and an analysis of the energy potential required for this process. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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20 pages, 4192 KiB

Open AccessArticle

Energy Consumption Forecasting in Korea Using Machine Learning Algorithms

by Sun-Youn Shin and Han-Gyun Woo

Energies 2022, 15(13), 4880; https://doi.org/10.3390/en15134880 - 2 Jul 2022

Cited by 46 | Viewed by 5051

Abstract

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In [...] Read more.

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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16 pages, 4999 KiB

Open AccessArticle

Combined Network Intrusion and Phasor Data Anomaly Detection for Secure Dynamic Control Centers

by André Kummerow, Kevin Schäfer, Parul Gupta, Steffen Nicolai and Peter Bretschneider

Energies 2022, 15(9), 3455; https://doi.org/10.3390/en15093455 - 9 May 2022

Cited by 6 | Viewed by 2456

Abstract

The dynamic operation of power transmission systems requires the acquisition of reliable and accurate measurement and state information. The use of TCP/IP-based communication protocols such as IEEE C37.118 or IEC 61850 introduces different gateways to launch cyber-attacks and to compromise major system operation [...] Read more.

The dynamic operation of power transmission systems requires the acquisition of reliable and accurate measurement and state information. The use of TCP/IP-based communication protocols such as IEEE C37.118 or IEC 61850 introduces different gateways to launch cyber-attacks and to compromise major system operation functionalities. Within this study, a combined network intrusion and phasor data anomaly detection system is proposed to enable a secure system operation in the presence of cyber-attacks for dynamic control centers. This includes the utilization of expert-rules, one-class classifiers, as well as recurrent neural networks to monitor different network packet and measurement information. The effectiveness of the proposed network intrusion and phasor data anomaly detection system is shown within a real-time simulation testbed considering multiple operation and cyber-attack conditions. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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22 pages, 473 KiB

Open AccessArticle

Optimal Well Control Based on Auto-Adaptive Decision Tree—Maximizing Energy Efficiency in High-Nitrogen Underground Gas Storage

by Edyta Kuk, Jerzy Stopa, Michał Kuk, Damian Janiga and Paweł Wojnarowski

Energies 2022, 15(9), 3413; https://doi.org/10.3390/en15093413 - 7 May 2022

Cited by 2 | Viewed by 2101

Abstract

To move the world toward a more sustainable energy future, it is crucial to use the limited hydrocarbon geological resources efficiently and to develop technologies that facilitate this. More rational management of petroleum reservoirs and underground gas storage can be obtained by optimizing [...] Read more.

To move the world toward a more sustainable energy future, it is crucial to use the limited hydrocarbon geological resources efficiently and to develop technologies that facilitate this. More rational management of petroleum reservoirs and underground gas storage can be obtained by optimizing well control. This paper presents a novel approach to optimal well control based on the combination of optimal control theory, innovative artificial intelligence methods, and numerical reservoir simulations. In the developed algorithm, well control is based on an auto-adaptive parameterized decision tree. Its parameters are optimized by state-of-the-art machine learning, which uses previous results to determine favorable parameters. During optimization, a numerical reservoir simulator is applied to compute the objective function. The developed solution enables full automation of the wells for optimal control. An exemplary application of the developed solution to optimize underground storage of gas with high nitrogen content confirmed its effectiveness. The total nitrogen content in the gas decreased by 2.4%, increasing energy efficiency without increasing expense, as only well control was modified. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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20 pages, 3959 KiB

Open AccessArticle

Effect of Climate on Residential Electricity Consumption: A Data-Driven Approach

by Cuihui Xia, Tandong Yao, Weicai Wang and Wentao Hu

Energies 2022, 15(9), 3355; https://doi.org/10.3390/en15093355 - 5 May 2022

Cited by 4 | Viewed by 3417

Abstract

Quantifying the climatic effect on residential electricity consumption (REC) can provide valuable insights for improving climate–energy damage functions. Our study quantifies the effect of climate on the REC in Tibet using machine learning algorithm models and model-agnostic interpretation tools of feature importance scores [...] Read more.

Quantifying the climatic effect on residential electricity consumption (REC) can provide valuable insights for improving climate–energy damage functions. Our study quantifies the effect of climate on the REC in Tibet using machine learning algorithm models and model-agnostic interpretation tools of feature importance scores and partial dependence plots. Results show that the climate contributes about 16.46% to total Tibet REC while socioeconomic factors contribute about 83.55%. Precipitation (particularly snowfall) boosts electricity consumption during the cold season. The effect of the climate is stronger in urban Tibet (~25.06%) than rural Tibet (~14.79%), particularly in September when electricity-aided heating is considered optional, as higher incomes amplified the REC response to the climate. With urbanization and income growth, the climate is expected to contribute more to Tibet REC. Hence, precipitation should be incorporated in climate–REC functions for the social cost of carbon (SCC) estimation, particularly for regions vulnerable to snowfall and blizzards. Herein, we developed a model-agnostic method that can quantify the total effect of the climate while differentiating between contributions from temperature and precipitation, which can be used to facilitate interdisciplinary and cross-section analysis in earth system science. Moreover, this data-driven model can be adapted to warn against extreme weather induced power outages. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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24 pages, 10776 KiB

Open AccessArticle

A Novel Microgrid Islanding Detection Algorithm Based on a Multi-Feature Improved LSTM

by Yan Xia, Feihong Yu, Xingzhong Xiong, Qinyuan Huang and Qijun Zhou

Energies 2022, 15(8), 2810; https://doi.org/10.3390/en15082810 - 12 Apr 2022

Cited by 15 | Viewed by 2496

Abstract

Islanding detection is one of the conditions necessary for the safe operation of the microgrid. The detection technology should provide the ability to differentiate islanded operations from power grid disturbances effectively. Given that it is difficult to set the fault threshold using the [...] Read more.

Islanding detection is one of the conditions necessary for the safe operation of the microgrid. The detection technology should provide the ability to differentiate islanded operations from power grid disturbances effectively. Given that it is difficult to set the fault threshold using the passive detection method, and because the traditional active detection method affects the output power quality, a microgrid islanding detection method based on the Sliding Window Discrete Fourier Transform (SDFT)-Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) network optimized by an attention mechanism is proposed. In this paper, the inverter output current and voltage at the point of common coupling (PCC) are transformed by the SDFT. The positive sequence, zero sequence, and negative sequence components of voltage and current harmonics are calculated and reconstructed by adopting the symmetrical component method (SCM). Meanwhile, the current and voltage are decomposed into a mono intrinsic mode function (IMF). The symmetric components of voltage, current, and IMFs are used as inputs to the deep learning algorithm. An LSTM with the features extracted to classify islanding and grid disturbance is proposed. By using the attention mechanism to set the weight values of the features of hidden states obtained by the LSTM network, the proportion of important features increases, which improves the classification effect. MATLAB/Simulink simulation results indicate that the proposed method can effectively classify the islanding state under different working conditions with an accuracy level of 98.4% and a loss value of 0.0725 with a maximal detection time of 66.94 ms. It can also reduce the non-detection zone (NDZ) and detection time and has a certain level of noise resistance. Meanwhile, the problem whereby the active method affects the microgrid power quality is avoided without disturbing the current or power of the microgrid. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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26 pages, 6184 KiB

Open AccessArticle

Detection and Diagnosis of Dependent Faults That Trigger False Symptoms of Heating and Mechanical Ventilation Systems Using Combined Machine Learning and Rule-Based Techniques

by Behrad Bezyan and Radu Zmeureanu

Energies 2022, 15(5), 1691; https://doi.org/10.3390/en15051691 - 24 Feb 2022

Cited by 14 | Viewed by 2798

Abstract

Detection and diagnosis of the malfunction of the heating, ventilation, and air conditioning (HVAC) systems result in more energy efficient systems with a higher level of indoor comfort. The information from the system combined with the artificial intelligence methods contributes to powerful fault [...] Read more.

Detection and diagnosis of the malfunction of the heating, ventilation, and air conditioning (HVAC) systems result in more energy efficient systems with a higher level of indoor comfort. The information from the system combined with the artificial intelligence methods contributes to powerful fault detection and diagnosis. The paper presents a novel method for the detection and diagnosis of multiple dependent faults in an air handling unit (AHU) of HVAC system of an institutional building during heating season. The proposed method guided the search for faults, by using the information and operation flow between sensors. Support vector regression (SVR) models, developed from building automation system (BAS) trend data, predicted air temperature of two target sensors, under normal operation conditions without known problems. The fault symptom was detected when the residual of measured and predicted values exceeded the threshold. The recurrent neural network (RNN) models predicted the normal operation values of regressor sensors, which were compared with measurements, as the first step for the identification of fault symptoms. Rule-based models were used for fault diagnosis of sensors or equipment. Results from a case study of an existing building showed the quality of proposed method for the detection and diagnosis of the multiple dependent faults. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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18 pages, 3121 KiB

Open AccessArticle

Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland

by Tomasz Cieślik, Piotr Narloch, Adam Szurlej and Krzysztof Kogut

Energies 2022, 15(4), 1393; https://doi.org/10.3390/en15041393 - 14 Feb 2022

Cited by 7 | Viewed by 2540

Abstract

In March 2020, a lockdown was imposed due to a global pandemic, which contributed to changes in the structure of the consumption of natural gas. Consumption in the industry and the power sector decreased while household consumption increased. There was also a noticeable [...] Read more.

In March 2020, a lockdown was imposed due to a global pandemic, which contributed to changes in the structure of the consumption of natural gas. Consumption in the industry and the power sector decreased while household consumption increased. There was also a noticeable decrease in natural gas consumption by commercial consumers. Based on collected data, such as temperature, wind strength, duration of weather events, and information about weather conditions on preceding days, models for forecasting gas consumption by commercial consumers (hotels, restaurants, and businesses) were designed, and the best model for determining the impact of the lockdown on gas consumption by the above-mentioned consumers was determined using the MAPE (mean absolute percentage error). The best model of artificial neural networks (ANN) gave a 2.17% MAPE error. The study found a significant decrease in gas consumption by commercial customers during the first lockdown period. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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19 pages, 61625 KiB

Open AccessArticle

Natural Gas Consumption Forecasting Based on the Variability of External Meteorological Factors Using Machine Learning Algorithms

by Wojciech Panek and Tomasz Włodek

Energies 2022, 15(1), 348; https://doi.org/10.3390/en15010348 - 4 Jan 2022

Cited by 14 | Viewed by 4197

Abstract

Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one [...] Read more.

Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one of the medium-sized cities in Poland. For this purpose, the machine learning algorithms, neural networks and two regression algorithms, MLR and Random Forest were used. Several variants of forecasting the demand for natural gas, with different lengths of the forecast horizon are presented and compared in this research. The results obtained using the MLR, Random Forest, and DNN algorithms show that for the tested input data, the best algorithm for predicting the demand for natural gas is RF. The differences in accuracy of prediction between algorithms were not significant. The research shows the differences in the impact of factors that create the demand for natural gas, as well as the accuracy of the prediction for each algorithm used, for each time horizon. Full article

(This article belongs to the Special Issue Energy and Artificial Intelligence)

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