Decision tree aided planning and energy balancing of planned community microgrids

Moutis, Panayiotis; Skarvelis‐Kazakos, Spyros; Brucoli, Maria

doi:10.1016/j.apenergy.2015.10.002

Cited by 45 publications

(19 citation statements)

References 40 publications

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“…Moutis et al (2016) [67] presented a novel tool for utilization of decision trees for planning storage systems in microgrids and controlling energy resources to balance energy for planned community microgrids. The presented methodology was validated by sensitivity analysis for several case studies.…”

Section: Decision Treesmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: Decision Treesmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…Some other interesting examples in the literature use the RF technique to classify data, showing high accuracy compared to other techniques [77]; In other examples [78] RF is used to improve, for instance, prediction of wind energy production in the short term, which is a complicated problem due to the stochastic nature of the wind and using the effects of seasonality. Other interesting examples can be found in [79], where two applications of Decision Trees techniques are presented: the planning of organized energy storage in microgrids and energy control within a PC through the optimal use of local energy resources. A complete case study shows properly the feasibility of this technique.…”

Section: Results Of the Prediction Error (As Inmentioning

confidence: 99%

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

2019

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In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.

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“…Data mining [13,[21][22][23][24][25] Artificial neural networks [7,[26][27][28][29][30][31][32] Support vector machine [33][34][35][36][37] Decision trees [14,21,[38][39][40][41][42][43] Energies 2019, 12, 4163…”

Section: Techniques Referencesmentioning

confidence: 99%

“…Moutis, P. [41] presents two applications of decision tree techniques: the planning of organized energy storage in microgrids and energy control within a PC through the optimal use of local energy resources, demonstrating through a case study the feasibility of this technique.…”

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confidence: 99%

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Bermejo¹,

Fernández

Pino

et al. 2019

Energies

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Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important effort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the different outputs for the different techniques.

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Decision tree aided planning and energy balancing of planned community microgrids

Cited by 45 publications

References 40 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

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