Energies

Research

13 pages, 1240 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

by Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Weiqing Chen and Abdulazeez Abdulraheem

Energies 2019, 12(19), 3671; https://doi.org/10.3390/en12193671 - 25 Sep 2019

Cited by 49 | Viewed by 5582

Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the [...] Read more.

Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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24 pages, 2186 KiB

Open AccessArticle

A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks

by Anna Gatzioura, Miquel Sànchez-Marrè and Karina Gibert

Energies 2019, 12(18), 3546; https://doi.org/10.3390/en12183546 - 16 Sep 2019

Cited by 14 | Viewed by 2532

Abstract

Recently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based [...] Read more.

Recently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based on predefined patterns, without taking the possible alternatives into account, usually due to the difficulty of properly evaluating them. Therefore, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find resources able to cover their needs, is still of high importance both for the companies and the whole ecosystem. In this work, we present a hybrid recommender system to support users in properly identifying the symbiotic relationships that might provide them an improved performance. This recommender combines a graph-based model for resource similarities, while it follows the basic case-based reasoning processes to generate resource recommendations. Several criteria, apart from resource similarity, are taken into account to generate, each time, the list of the most suitable solutions. As highlighted through a use case scenario, the proposed system could play a key role in the emerging industrial symbiotic platforms, as the majority of them still do not incorporate automatic decision support mechanisms. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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20 pages, 4101 KiB

Open AccessArticle

“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

by Jaume Manero, Javier Béjar and Ulises Cortés

Energies 2019, 12(12), 2385; https://doi.org/10.3390/en12122385 - 21 Jun 2019

Cited by 24 | Viewed by 4123

Abstract

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around [...] Read more.

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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23 pages, 559 KiB

Open AccessArticle

An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

by L. Alvarado-Barrios, A. Rodríguez del Nozal, A. Tapia, J. L. Martínez-Ramos and D. G. Reina

Energies 2019, 12(11), 2143; https://doi.org/10.3390/en12112143 - 4 Jun 2019

Cited by 19 | Viewed by 3153

Abstract

In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and [...] Read more.

In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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20 pages, 1377 KiB

Open AccessArticle

Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models

by Mohammad Hossein Ahmadi, Mohammad Dehghani Madvar, Milad Sadeghzadeh, Mohammad Hossein Rezaei, Manuel Herrera and Shahaboddin Shamshirband

Energies 2019, 12(10), 1916; https://doi.org/10.3390/en12101916 - 19 May 2019

Cited by 26 | Viewed by 3910

Abstract

Currently, one of the biggest concerns of human beings is greenhouse gas emissions, especially carbon dioxide emissions in developed and under-developed countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount [...] Read more.

Currently, one of the biggest concerns of human beings is greenhouse gas emissions, especially carbon dioxide emissions in developed and under-developed countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO

_{2}

emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay. The studied region is modelled based on the available input data in terms of million tons including oil (million tons), gas (million tons oil equivalent), coal (million tons oil equivalent),

R_{e w}

(million tons oil equivalent) and Gross Domestic Product (GDP) in terms of billion U.S. dollars. Moreover, the available patents in the field of climate change mitigation in six Latin American countries, namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay, have been reviewed and analysed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies, which account for 60% and 72%, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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21 pages, 1136 KiB

Open AccessArticle

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

by Raúl Parada, Jordi Font and Jordi Casas-Roma

Energies 2019, 12(10), 1832; https://doi.org/10.3390/en12101832 - 14 May 2019

Cited by 1 | Viewed by 2826

Abstract

Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual [...] Read more.

Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual rainfalls and other natural events, and consequently, the energy generation. Therefore, forecasting techniques are helpful to predict water level, and thus, electricity production. This paper examines state-of-the-art methods to predict the water level in Catalan reservoirs comparing two approaches: using the water level uniquely, uni-variant; and adding meteorological data, multi-variant. With respect to relating works, our contribution includes a longer times series prediction keeping a high precision. The results return that combining Support Vector Machine and the multi-variant approach provides the highest precision with an

R^{2}

value of 0.99. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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17 pages, 427 KiB

Open AccessArticle

Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection

by Bernat Coma-Puig and Josep Carmona

Energies 2019, 12(9), 1748; https://doi.org/10.3390/en12091748 - 8 May 2019

Cited by 38 | Viewed by 3761

Abstract

The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount [...] Read more.

The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount of data with smart algorithms. In this work we explore the possibilities that exist in the implementation of Machine-Learning techniques for the detection of Non-Technical Losses in customers. The analysis is based on the work done in collaboration with an international energy distribution company. We report on how the success in detecting Non-Technical Losses can help the company to better control the energy provided to their customers, avoiding a misuse and hence improving the sustainability of the service that the company provides. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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15 pages, 2463 KiB

Open AccessArticle

A Residential House Comparative Case Study Using Market Available Smart Plugs and EnAPlugs with Shared Knowledge

by Luis Gomes, Filipe Sousa, Tiago Pinto and Zita Vale

Energies 2019, 12(9), 1647; https://doi.org/10.3390/en12091647 - 30 Apr 2019

Cited by 5 | Viewed by 2487

Abstract

Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that [...] Read more.

Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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14 pages, 1540 KiB

Open AccessEditor’s ChoiceArticle

Environmental Decision Support System for Biogas Upgrading to Feasible Fuel

by Eric Santos-Clotas, Alba Cabrera-Codony, Alba Castillo, Maria J. Martín, Manel Poch and Hèctor Monclús

Energies 2019, 12(8), 1546; https://doi.org/10.3390/en12081546 - 24 Apr 2019

Cited by 20 | Viewed by 3556

Abstract

Biogas production is a growing market and the existing conversion technologies require different biogas quality and characteristics. In pursuance of assisting decision-makers in biogas upgrading an environmental decision support system (EDSS) was developed. Since the field is rapidly progressing, this tool is easily [...] Read more.

Biogas production is a growing market and the existing conversion technologies require different biogas quality and characteristics. In pursuance of assisting decision-makers in biogas upgrading an environmental decision support system (EDSS) was developed. Since the field is rapidly progressing, this tool is easily updatable with new data from technical and scientific literature through the knowledge acquisition level. By a thorough technology review, the diagnosis level evaluates a wide spectrum of technologies for eliminating siloxanes, H₂S, and CO₂ from biogas, which are scored in a supervision level based upon environmental, economic, social and technical criteria. The sensitivity of the user towards those criteria is regarded by the EDSS giving a response based on its preferences. The EDSS was validated with data from a case-study for removing siloxanes from biogas in a sewage plant. The tool described the flow diagram of treatment alternatives and estimated the performance and effluent quality, which matched the treatment currently given in the facility. Adsorption onto activated carbon was the best-ranked technology due to its great efficiency and maturity as a commercial technology. On the other hand, biological technologies obtained high scores when economic and environmental criteria were preferred. The sensitivity analysis proved to be effective allowing the identification of the challenges and opportunities for the technologies considered. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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Graphical abstract

18 pages, 656 KiB

Open AccessArticle

Feature Selection Algorithms for Wind Turbine Failure Prediction

by Pere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó and Jordi Solé-Casals

Energies 2019, 12(3), 453; https://doi.org/10.3390/en12030453 - 31 Jan 2019

Cited by 38 | Viewed by 4671

Abstract

It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points [...] Read more.

It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines. Full article

(This article belongs to the Special Issue Artificial Intelligence in Data Science For Energy Management in Sustainability)

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