Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Massaro, Alessandro; Starace, Giuseppe

doi:10.3390/s22134929

Cited by 13 publications

(8 citation statements)

References 107 publications

(122 reference statements)

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“…The adopted Convolutional Neural Network (CNN) model is developed by Keras (Ten-sorFlow) Deep Learning Python libraries integrated into Konstanz Information Miner (KN-IME) workflow. KNIME is a versatile tool useful for testing supervised algorithms [75][76][77][78][79] and also predicting energy in renewable energetic sources [80,81]. The optimized CNN network is characterized by the following hyperparameters: an input layer with 193 inputs (the input matrix dataset is transposed to obtain as rows the temporal succession and as attributes the name of the nations), a dense layer (193 neurons adopting a ReLU activation function), an output dense layer (193 outputs predicting the next two years using Softmax as activation function), 80% of the dataset as training dataset (from year 2022 to year 2018), 20% of the remaining dataset as a testing dataset (data from last two years, 2019 and 2020), 100 epochs, training batch size equals to 100, Adam optimizer (0.001 as learning rate, beta1 equals to 0.9, beta2 equals to 0.999, epsilon equals to 1E−8.0 as learning rate decay).…”

Section: Deep Learning and Convolutional Neural Network-cnn For The E...mentioning

confidence: 99%

The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective

Laureti¹,

Massaro²,

Costantiello³

et al. 2023

Sustainability

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In this article, we investigate the impact of “Renewable Electricity Output” on the green economy in the context of the circular economy for 193 countries in the period 2011–2020. We use data from the World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Last Squares-WLS, and Pooled Ordinary Least Squares-OLS. Our results show that Renewable Electricity Output is positively associated, among others, with “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, with “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. In adjunct, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries. Furthermore, we improve the machine learning analysis with a Deep Learning approach using Convolutional Neural Network-CNN but the algorithm is not appropriate for the analyzed dataset. Less complex machine learning algorithms show better statistical results.

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Section: Deep Learning and Convolutional Neural Network-cnn For The E...mentioning

confidence: 99%

The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective

Laureti¹,

Massaro²,

Costantiello³

et al. 2023

Sustainability

Self Cite

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“…The insolation and cell temperature of solar panels primarily define the total generated power by a solar plant. In the research reviews, a whole array of differing MPPT algorithms has been revealed [ 1 , 2 , 4 ]. Among them, the perturbation and observation (P and O) and incremental conductance (INC) algorithms are the most popular due to their easy and simple implementation.…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

“…There are a lot of GO algorithms to create a GMPPT model [ 1 , 2 , 4 ], but all these models have the following disadvantages: power oscillations in the calm mode; the initialization is a critical issue that decrease power; very slow convergence to a GMPP under insolation’s variation, etc. Due to all the above-mentioned disadvantages, GO-based, real-time GMPPT of a solar plant are ineffective while ML technologies provide the required performance.…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

“…Within breakthrough studies, ML technologies collected, analyzed, and converted a huge number of sensory datasets into ML knowledge. These big data sets are collected by supervisory control and data acquisition (SCADA) systems [ 4 ]. The SCADA system is able to integrate the sensor system and ML technologies into an ML sensor system based on software that implements ML sensor models and integrates with SCADA through API.…”

Section: Introductionmentioning

confidence: 99%

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A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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“…Energy balancing, KPI for energy, produced current/voltage (renewable energy systems), correct setting of energy tools (energy of inverters, power of electrical transformers, correct orientation and efficiency of photovoltaic panels, wind turbine vibrations [21], etc.) [22].…”

Section: Energy Productionmentioning

confidence: 99%

Multi-Level Decision Support System in Production and Safety Management

Massaro

2022

Knowledge

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The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining digital knowledge by means of logics that are able to select the best decisions. The PM model is applied to an open dataset simulating a working scenario and defining a possible safety control method based on the risk assessment. The application of the PM model provides automatic alerting conditions based on a threshold of values detected by sensors. Specifically, the PM model is applied to worker security systems characterized by the environment with a risk of emission of smoke and gases. The PM model is improved by Artificial Intelligence (AI) algorithms by strengthening information through prediction results and improving the risk analysis. An Artificial Neural Network (ANN) MultilaLayer Perceptron (MLP) algorithm is adopted for the risk prediction by achieving the good computational performance of Mean Absolute Error (MAE) of 0.001. The PM model is first sketched by the Business Process Modelling and Notation (BPMN) method, and successively executed by means of the Konstanz Information Miner (KNIME) open source tool, implementing the process-controlling risks for different working locations. The goal of the paper is to apply the theoretical PM model by means of open source tools by enhancing how the multi-level approach is useful for defining a security procedure to control indoor worker environments. Furthermore, the article describes the key variables able to control production and worker safety for different industry sectors. The presented DSS PM model also can be applied to industry processes focused on production quality.

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Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Cited by 13 publications

References 107 publications

The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective

The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective

A Review on Machine Learning Applications for Solar Plants

Multi-Level Decision Support System in Production and Safety Management

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