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To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper. [ABSTRACT FROM AUTHOR]

Suter curves for the Wh and Wm characteristics and four-quadrant (4Q) diagrams of 11 radial pump–turbine models with different specific speeds (nq = 24.34, 24.8, 27, 28.6, 38, 41.6, 41.9, 43.83, 50, 56, and 64.04) are presented for the first time in this paper, as well as Suter curves for two pump models (nq = 25 and 41.8) previously published in the literature. All of these curves were analyzed to establish a certain universal law of behavior, depending on the specific speed. To determine such a law, a fitting procedure using regression and spline methods was carried out. This paper provides details of a research plan and structures (including data collection for four-quadrant diagrams for pump–turbine and pump models under different specific speeds nq), a procedure for re-calculating four-quadrant diagrams of the models as Suter curves for the Wh and Wm characteristics, definitions of the optimal points for pump and turbine operating modes in pump–turbine models under different specific speeds, and the development of numerical models in MATLAB to obtain a universal equation for the Wh and Wm characteristics. The scientific contribution of this paper is that it is the first to publish original mathematical curves using universal equations for the Wh and Wm characteristics of radial pumps and pump–turbines. The applicability of the equations is demonstrated by considering a pumping station in which two radial pumps were installed, for which the calculation of transient processes was performed using a numerical model developed in MATLAB by the authors. The transition process results are compared for two cases: first, when input data in the numerical model are used with the values of the Suter curves for the Wh and Wm characteristics obtained by re-calculating the four-quadrant operating characteristics (Q11, n11, M11) at a given specific speed, and second, when the values of the Suter curves for the Wh and Wm characteristics are obtained from the universal equations. [ABSTRACT FROM AUTHOR]

Aiming at the problem of accurate AC series arc fault detection, this paper proposes a low voltage AC series arc fault intelligent detection model based on deep learning. According to the GB/T 31143—2014 standard, an experimental platform was established. This system comprises a lower computer (slave computer) and an upper computer (master computer). It facilitates the acquisition of experimental data and the detection of arc faults during the data acquisition process. Based on a one-dimensional Convolutional Neural Network (CNN), Residual model (Res) and RIME optimization algorithm (RIME) are introduced to optimize the CNN. The current signals collected using high-frequency current, low-frequency coupled current, and high-frequency coupled current are used to construct an arc fault feature set for training of the necessary detection model. The experimental results indicate that the RIME optimization algorithm delivers the best performance when optimizing a one-dimensional CNN detection model with an introduced Res. This model achieves a detection accuracy of 99.42% ± 0.13% and a kappa coefficient of 95.69% ± 0.96%. For collection methods, high-frequency coupled current signals are identified as the optimal choice for detecting low-voltage AC series arc faults. Regarding feature selection, random forest-based feature importance ranking proves to be the most effective method. [ABSTRACT FROM AUTHOR]

Malaysia's energy intensity (GWh/GDP) shows an increasing trend since the 1990s, leading to the government's efforts to promote energy efficiency via policies such as the National Energy Efficiency Action Plan (NEEAP), which includes the Energy Performance Contracting (EPC) initiative. This paper reviews recent publications in industrial Combined Heat and Power (CHP) with a focus on international case studies relevant to Malaysian industries that use industrial steam and highlights trends within the research area. It also provides the basis for more case studies to be performed in the Malaysian industry to improve energy efficiency while also supporting further academic research in the area. Additionally, the paper documents the importance of data collection and analysis as well as demand forecasting, not only for a better understanding of industrial energy systems but also to increase profitability since system loads may vary throughout a typical year. A multi-criteria and comprehensive approach is recommended in future case studies to ensure energy efficiency, economic returns and environmental impact are considered to ensure long-term sustainability. A summary of barriers to CHP implementation in the industry is also included to provide a broad understanding of industrial CHP. [ABSTRACT FROM AUTHOR]

The availability of empirical energy data from Advanced Metering Infrastructure (AMI)—which includes household smart meters—has enabled residential energy demand to be characterised in different forms. This paper first presents a literature review of applications of measured electricity, gas, and heat consumption data at a range of temporal resolutions, which have been used to characterise and develop an understanding of residential energy demand. User groups, sectors, and policy areas that can benefit from the research are identified. Multiple residential energy demand datasets have been collected in the UK that enable this characterisation. This paper has identified twenty-three UK datasets that are accessible for use by researchers, either through open access or defined processes, and presents them in an inventory containing details about the energy data type, temporal and spatial resolution, and presence of contextual physical and socio-demographic information. Thirteen applications of data relating to characterising residential energy demand have been outlined in the literature review, and the suitability of each of the twenty-three datasets was mapped to the thirteen applications. It is found that many datasets contain complementary contextual data that broaden their usefulness and that multiple datasets are suitable for several applications beyond their original project objectives, adding value to the original data collection. [ABSTRACT FROM AUTHOR]

The goal of this work is to give a full review of how machine learning (ML) is used in thermal comfort studies, highlight the most recent techniques and findings, and lay out a plan for future research. Most of the researchers focus on developing models related to thermal comfort prediction. However, only a few works look at the current state of adaptive thermal comfort studies and the ways in which it could save energy. This study showed that using ML control schemas to make buildings more comfortable in terms of temperature could cut energy by more than 27%. Finally, this paper identifies the remaining difficulties in using ML in thermal comfort investigations, including data collection, thermal comfort indices, sample size, feature selection, model selection, and real-world application. [ABSTRACT FROM AUTHOR]

With recent advancements in data technologies, particularly machine learning, research focusing on the enhancement of energy efficiency in residential, commercial, and industrial settings through the collection of load data, such as heat, electricity, and gas, has gained significant attention. Nevertheless, issues arising from hardware- or network-related problems can result in missing data, necessitating the development of management techniques to mitigate these challenges. Traditional methods for missing imputation face difficulties when operating in constrained environments characterized by short data collection periods and frequent consecutive missing. In this paper, we introduce the denoising masked autoencoder (DMAE) model as a solution to improve the handling of missing data, even in such restrictive settings. The proposed DMAE model capitalizes on the advantages of the denoising autoencoder (DAE), enabling effective learning of the missing imputation process, even with relatively small datasets, and the masked autoencoder (MAE), allowing for learning in environments with a high missing ratio. By integrating these strengths, the DMAE model achieves an enhanced performance in terms of missing imputation. The simulation results demonstrate that the proposed DMAE model outperforms the DAE or MAE significantly in a constrained environment where the duration of the training data is short, less than a year, and missing values occur frequently with durations ranging from 3 h to 12 h. [ABSTRACT FROM AUTHOR]

Despite activities to introduce low-carbon energy sources worldwide, the share of conventional facilities burning organic fuels remains high. One approach to address this problem is to look for solutions to reduce energy consumption. There are various research projects in the area of energy efficiency that lead to diverse results—such as models, methodologies, new data and theories. On the other hand, induction motor drives are becoming a major consumer of electric power because of their wide range of applications. In this paper, after careful selection and systematization of 151 literature sources, an extensive study and criteria analysis of the existing state of affairs in the area of energy efficiency improvement of induction motor drives has been carried out. Five major and 48 minor research areas in this field have been identified. The results show that issues related to the adaptation of scientific results and the conditions for their effective and wide-ranging application in practice have not been discussed and investigated so far. Adaptation should take into account the possibilities of data acquisition, including data from measurements; the competences of energy managers; and the type of information provided to them. Based on the seven conclusions formulated below, summary recommendations are made to direct future research towards the justification of models for increasing the power efficiency of induction drives, adapted for use by energy managers. [ABSTRACT FROM AUTHOR]

The double-end partial discharge (PD) measurement method is the most common method for measuring and localizing PD sources in power cables. The sensitivity of the PD sensor, the processing speed of the data acquisition unit, and the method of the PD localization algorithm are the three main keys to ensuring the accuracy of the PD source localization on power cables. A new multi-end PD localization algorithm known as segmented correlation trimmed mean (SCTM) has recently demonstrated excellent accuracy in the localization of PD sources on power cables. The algorithm, however, is only applicable to multi-end PD measurement methods. In this paper, the mathematical equation of the SCTM algorithm is customized to match the double-end PD measurement method. A MATLAB simulation was conducted to assess the performance of the SCTM algorithm in the double-end PD measurement method. The maximum peak detection (MPD) algorithm, segmented correlation (SC), and SCTM algorithm were compared as PD localization algorithms. The SC algorithms have shown that identifying the correlation bond between two cues instead of the peak of the PD signal in the MPD algorithm significantly increases the PD localization accuracy. The results show that the SCTM algorithm outperforms the MPD and SC algorithms in terms of accuracy. [ABSTRACT FROM AUTHOR]

Deep geothermal energy can aid in the decarbonization of heat within the UK; this is required to meet net zero carbon emissions targets by 2050. The Cheshire Basin represents a significant opportunity for the development of deep geothermal resources; there are vast quantities of high permeability sandstones in hydraulic continuity, with temperatures favorable for direct heat use and, potentially, for power generation. Newly produced basal temperature maps in this study indicate the likely maximum temperatures for the basin, with the hottest temperature expected to be between 100 and 131.2 °C in the Crewe area. There have also previously been a range of estimates highlighting a geothermal resource within the basin to be in the region of 44.1 to 75 × 1018 J; however, previous estimates for heat in place are limited to simple volumetric or geometrical constraints. Therefore, this paper uses digitized depth and temperature maps to provide new estimates for the heat in place. Results suggest the resource has been underestimated and there is a need for more detailed evaluation. Depending on the geothermal gradient, the resource could be between 91 and 144 × 1018 J (1.26 to 1.45 × 1017 J/km2). Although there is a significant amount of heat in place, geological issues preventing development remain, such as the uncertainty in the quality of the reservoir at depth due to data limitations and the lateral continuity of the Manchester Marls Formation, which could act as a barrier to flow. Nevertheless, further regional assessment of the basin and data acquisition is required to build confidence in the reservoir quality and reduce uncertainty. This could unlock the basin for geothermal development. [ABSTRACT FROM AUTHOR]

For newly developed measuring systems, it is easy to estimate type-B uncertainties based on technical data from the measuring modules applied. However, it is difficult to estimate A-type uncertainties due to the unknown type and level of interferences infiltrating the measuring system. This is a particularly important problem for measurements carried out in the presence of typical of power grid disturbances. The aim of the research was to develop a method and a measurement stand for experimental assessment of uncertainties in a measuring system that makes use of data acquisition modules containing analog-to-digital converters (ADCs). The paper describes, in detail, the design of a completed test stand. It presents an original application in the LabVIEW environment, which enables testing the dependence of the uncertainties with the quantity of the measurements averaged in a series, for different kinds and levels of interferences infiltrating the measuring path. The results of tests for several popular measuring modules are presented. An analysis of the determined uncertainties was carried out in relation to the parameters of the tested measurement modules and for various levels of interferences. It is proved that an increase in the number of averaged measurements to approx. 100–200 always results in a decrease in uncertainty for each tested module and under all conditions. However, a further increase in the quantity of measurements, even up to 1000 averaged measurements, proved reasonable only for high-accuracy modules, in particular with a high level of interferences. An excessive increase in the quantity of averaged measurements proved a low effect for modules characterised by a low resolution and with a low level of interferences. The measurement results also proved that when estimated, uncertainties in the interference probability distribution are significant, especially if they deviate from normal distribution. [ABSTRACT FROM AUTHOR]

In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants. [ABSTRACT FROM AUTHOR]

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users' charging requirements, the EV aggregator's power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users' requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users' requirements, and improving the system frequency stability. [ABSTRACT FROM AUTHOR]

Different multilevel converter topologies have been presented for achieving more output voltage steps, hence improving system performance and lowering costs. In this paper, a hybrid multilevel inverter (MLI) topology is proposed for active-power-filter applications. The proposed MLI is a combination of two standard topologies: the cascaded H-bridge and the three-phase cascaded voltage source inverter. This configuration enhances the voltage levels of the proposed MLI while using fewer switches than typical MLI topologies. The proposed MLI was developed in the MATLAB/Simulink environment, and a closed-loop control technique was used to achieve a unity power factor connection of the PV modules to the grid, as well as to compensate for harmonics caused by nonlinear loads. To demonstrate that the configuration was working correctly and that the control was precise, the proposed MLI was constructed in a laboratory. A MicroLabBox real-time controller handled data acquisition and switch gating. The proposed topology was experimentally connected to the grid and the MLI was experimentally used as an active power filter to compensate for the harmonics generated due to nonlinear loads. This control technique was able to generating a sinusoidal grid current that was in phase with the grid voltage, and the grid current's total harmonic distortion was within acceptable limits. To validate the practicability of the proposed MLI, both simulation and experimental results are presented. [ABSTRACT FROM AUTHOR]

In order to realize the intelligent management of a power materials warehouse, the Internet of Things based on wireless sensor networks (WSNs) is a promising effective solution. Considering the limited battery capacity of sensor nodes, the optimization of the topology control and the determination of the amount of collected data are critical for prolonging the survival time of WSNs and increasing the satisfaction of the warehouse supplier. Therefore, in this paper, an optimization problem on sensor association and acquisition data satisfaction is proposed, and the subproblem of the sensor association is modeled as the knapsack problem. To cope with it, the block coordinate descent method is used to obtain the suboptimal solution. A sensor association scheme based on the ant colony algorithm (ACO) is proposed, and the upper and lower bounds of this optimization problem are also obtained. After this, a cluster head selection algorithm is given to find the optimal cluster head. Finally, the experimental simulations show that the algorithms proposed in this paper can effectively improve the energy utilization of WSNs to ensure the intelligent management of a power materials warehouse. [ABSTRACT FROM AUTHOR]

It remains unclear whether the decision to cook with both polluting and cleaner-burning fuels ('fuel stacking') serves as a transition phase towards the full adoption of clean-cooking practices, or whether stacking allows households to enhance fuel security and choose from a variety of cooking technologies and processes. This paper offers a unique contribution to the debate by positioning fuel stacking as the central research question in the exploration of existing household survey data. This research analyses the World Bank's Multi-Tier Framework survey data concerning energy access and cooking practices in Cambodia, Myanmar, and Zambia. Its novel approach uses fuel expenditure data to group urban households according to the intensity of biomass consumption (wood, charcoal) relative to modern fuel consumption (electricity, gas). The research explores how different fuel-stacking contexts are associated with factors related to household finances, composition, experiences of electricity, and attitudes towards modern fuels. This study shows the diversity of characteristics and behaviours associated with fuel stacking in urban contexts, thus demonstrating the need for fuel stacking to feature prominently in future data collection activities. The paper ends with five key recommendations for further research into fuel stacking and its role in clean-cooking transitions. [ABSTRACT FROM AUTHOR]

A transition to clean fuels and technology for cooking is increasingly recognised as a cornerstone of sustainable development. However, sufficient, appropriate, affordable finance to support the transition is lacking. Grounded in primary data collection via expert interviews, this study's research objective was to critically assess development finance institutions' (DFIs) delivery of climate and development finance to address cooking poverty. Interview findings underscore DFIs' important role in the transition, including to create the ecosystem conditions conducive to sustained investment. However, as a group they are not demonstrating the risk appetite and financial solutions that clean cooking markets need. Nor are they operating with the agility and flexibility required for rapid scale-up. Consequently, DFIs are not optimally fulfilling their mandates to create additionality and mobilise private capital in these markets. Interviewees call for DFIs to reconsider their approach, and we rely on these findings to posit a theory of change for clean cooking finance. [ABSTRACT FROM AUTHOR]

In this paper, we present a detailed analysis of walkaway vertical seismic profiling (VSP) data, which can be used to obtain Thomsen parameters using P-wave-only inversion. Data acquisition took place in difficult field conditions, which influenced the quality of the data. Therefore, this paper also shows a seismic data processing scheme that allows the estimation of correct polarization angles despite poor input data quality. Moreover, we showed that it is possible to obtain reliable and detailed values of Thomsen's anisotropy parameters for data that are challenging due to extremely difficult field conditions during acquisition and the presence of an overburden of salt and anhydrite (Zechstein formation). This complex is known for its strong seismic signal-attenuating properties. We designed a special processing workflow with a signal-matching procedure that allows reliable estimation of polarization angles for low-quality data. Additionally, we showed that P-wave-only inversion for the estimation of local anisotropy parameters can be used as valuable additional input for detailed interpretation of geological media, even if anisotropy is relatively low. [ABSTRACT FROM AUTHOR]

In order to find the causes of statistical line loss abnormalities and propose better loss reduction strategies, it is necessary to improve the accuracy of theoretical line loss calculations. Since Distributed Generation (DG) access to the distribution network causes variability of power flow in the distribution network within a short period, the error of the traditional line loss calculation method increases. For the line loss calculation of medium-voltage distribution networks containing DGs with high-density collection data, a continuous line loss calculation method for the distribution network was proposed, aiming at improving the accuracy compared with the traditional line loss calculation method. The proposed method makes full use of the high-density collection data on the dispatch side and DG side, distributing the supply power to each load node by power to calculate real-time power flow, thus obtaining a more credible line loss value. The effectiveness and accuracy of the proposed method were verified in the IEEE 17-node distribution system. [ABSTRACT FROM AUTHOR]

This paper presents a review of the main electrical components that are expected to be present in marine renewable energy arrays. The review is put in context by appraising the current needs of the industry and identifying the key components required in both device and array-scale developments. For each component, electrical, mechanical and cost considerations are discussed; with quantitative data collected during the review made freely available for use by the community via an open access online repository. This data collection updates previous research and addresses gaps specific to emerging offshore technologies, such as marine and floating wind, and provides a comprehensive resource for the techno-economic assessment of offshore energy arrays. [ABSTRACT FROM AUTHOR]

This work studied the energy behavior of six matrix multiplication algorithms with various physical asset usage patterns. Two were variants of the straight inner product of rows and columns. The rest were variants of Strassen's divide-and-conquer. Cases varied in ways that were expected to affect energy behavior. The study collected data for square matrix dimensions up to 4000. The research used reliable on-chip integrated voltage regulators embedded in a recent HPC-class AMD CPU for power measurements. Inner product methods used much less energy than the others for small to moderately large matrices. The advantage diminished for sufficiently large dimensions. The power draw of the inner product methods was less for small dimensions. After a point, the power advantage shifted significantly in favor of the divide-and-conquer group (average of 24% better), with the more block-optimized versions showing increased power efficiency (at least 8.3% better than the base method). The study explored the interplay between algorithm design, power efficiency, and computational resources. It aims to help advance the cause of power efficiency in HPC and other scenarios that rely on this vital computation. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a 16F877 microcontroller-based wireless data acquisition system and a study of the feasibility of different existing methodologies linked to field data acquisition from remote photovoltaic (PV) water pumping systems. Various existing data transmission techniques were studied, especially satellite, radio, Global System for Mobile Communication (GSM) and General Packet Radio Service (GPRS). The system's hardware and software and an application to test its performance are described. The system will be used for reading, storing and analyzing information from several PV water pumping stations situated in remote areas in the arid region of the south of Tunisia. The remote communications are based on the GSM network and, in particular, on the Short text Message Service (SMS). With this integrated system, we can compile a complete database of the different parameters related to the PV water pumping systems of Tunisia. This data could be made available to interested parties over the Internet. [ABSTRACT FROM AUTHOR]

Smart transportation a smart city application where traditional individual models are transforming to shared and distributed ownership. These models are used to serve commuters for inter- and intra-city travel. However, short-range urban transportation services within campuses, residential compounds, and public parks are not explored to their full capacity compared to the distributed vehicle model. This paper aims to explore and design an adequate framework for battery-operated shared mobility within a large community for short-range travel. This work identifies the characteristics of the shared mobility for battery-operated vehicles and accordingly proposes an adequate solution that deals with real-time data collection, tracking, and automated decisions. Furthermore, given the requirement for real-time decisions with low latency for critical requests, the paper deploys the proposed framework within the 3-tier computing model, namely edge, fog, and cloud tiers. The solution design considers the power consumption requirement at the edge by offloading the computational requests to the fog tier and utilizing the LoRaWAN communication technology. A prototype implementation is presented to validate the proposed framework for a university campus using e-bikes. The results show the scalability of the proposed design and the achievement of low latency for requests that require real-time decisions. [ABSTRACT FROM AUTHOR]

Set against the United Nations Sustainable Development Goal 7, and the need to increase biomass Improved Cookstove (ICS) adoption and sustained use across the globe, this paper presents an evaluation of Practical Action Nepal's (PAN) Results Based Financing for Improved Cookstove Market Development in Nepal (RBF) project, which was conducted between January and April 2020. Nepal has a long history of International Development assistance, yet 65.8% of rural households still use firewood as their primary source of energy. With this in mind we aimed to understand the barriers, enablers and engagement strategies for the adoption and sustained use of Improved Cookstoves (ICS), map key stakeholder role perceptions and interactions, and identify areas for improvement to increase the sustained use of ICS in the focus communities. This paper uses the methodological approach from the qualitative Technology Implementation Model for Energy (TIME) for the data collection and analysis elements. Our core results show a direct need for improved communication between all key stakeholder groups, the impact of demand and supply side financial incentives in creating reputational risk for community-based key stakeholders, and how the RBF mechanism promotes initial end-user adoption but not sustained use of ICS due to a focus on immediate results. [ABSTRACT FROM AUTHOR]

Low-enthalpy geothermal energy can make a major contribution towards reducing CO2 emissions. However, the development of geothermal reservoirs is costly and time intensive. In particular, high capital expenditures, data acquisition costs, and long periods of time from identifying a geothermal resource to geothermal heat extraction make geothermal field developments challenging. Conventional geothermal field development planning follows a linear approach starting with numerical model calibrations of the existing subsurface data, simulations of forecasts for geothermal heat production, and cost estimations. Next, data acquisition actions are evaluated and performed, and then the models are changed by integrating the new data before being finally used for forecasting and economics. There are several challenges when using this approach and the duration of model rebuilding with the availability of new data is time consuming. Furthermore, the approach does not address sequential decision making under uncertainty as it focuses on individual data acquisition actions. An artificial intelligence (AI)-centric approach to field development planning substantially improves cycle times and the expected rewards from geothermal projects. The reason for this is that various methods such as machine learning in data conditioning and distance-based generalized sensitivity analysis assess the uncertainty and quantify its potential impact on the final value. The use of AI for sequential decision making under uncertainty results in an optimized data acquisition strategy, a recommendation of a specific development scenario, or advice against further investment. This approach is illustrated by applying AI-centric geothermal field development planning to an Austrian low-enthalpy geothermal case. The results show an increase in the expected value of over 27% and a reduction in data acquisition costs by more than 35% when compared with conventional field development planning strategies. Furthermore, the results are used in systematic trade-off assessments of various key performance indicators. [ABSTRACT FROM AUTHOR]

It is common knowledge that wind energy is a crucial, strategic component of the mix needed to create a green economy. In this regard, optimizing the operations and maintenance (O&M) of wind turbines (WTs) is key, as it will serve to reduce the levelized cost of electricity (LCOE) of wind energy. Since most modern WTs are equipped with a Supervisory Control and Data Acquisition (SCADA) system for remote monitoring and control, condition-based maintenance using SCADA data is considered a promising solution, although certain drawbacks still exist. Typically, large amounts of normal-operating SCADA data are generated against small amounts of fault-related data. In this study, we use high-frequency SCADA data from an operating WT with a significant imbalance between normal and fault classes. We implement several resampling techniques to address this challenge and generate synthetic generator fault data. In addition, several machine learning (ML) algorithms are proposed for processing the resampled data and WT generator fault classification. Experimental results show that ADASYN + Random Forest obtained the best performance, providing promising results toward wind farm O&M optimization. [ABSTRACT FROM AUTHOR]

The study of geological CO2 sequestration and its long-term implications are crucial for ensuring the safety and sustainability of carbon capture and storage (CCS) projects. This work presents a numerical reservoir modeling study to upscale CO2 injection in the Eastern Illinois Basin to a cumulative value of 27 Mt within the next 20 years, adding one proposed Class VI injector well to the two already existing ones in this field. Along with the reservoir simulations that include the main CO2 trapping mechanisms that ensure a minimum of a 100-year Area-of-Review containment, we describe a step-by-step approach to enhance measurement, monitoring, and verification (MMV) plans, starting from low-cost methods such as repeated 1D VSP in existing boreholes to 2D seismic surveys and higher-cost data acquisition techniques. [ABSTRACT FROM AUTHOR]

Solar energy is the most promising renewable resource with an unbounded energy source, capable of meeting all human energy requirements. Solar Photovoltaic (SPV) is an effective approach to convert sunlight into electricity, and it has a promising future with consistently rising energy demand. In this work, we propose a smart solution of outdoor performance characterization of the SPV module utilizing a robust, lightweight, portable, and economical Outdoor Test Facility (OTF) with the Internet of Things (IoT) capability. This approach is focused on the capacitive load-based method, which offers improved accuracy and cost-effective data logging using Raspberry Pi and enables the OTF to sweep during the characterization of the SPV module automatically. A demonstration using an experimental setup is also provided in the paper to validate the proposed OTF. This paper further discusses the advantages of using the capacitive load approach over the resistive load approach. IoT's inherent benefits empower the proposed OTF method on the backgrounds of real-time tracking, data acquisition, and analysis for outdoor output performance characterization by capturing Current–Voltage (I–V) and Power–Voltage (P–V) curves of the SPV module. [ABSTRACT FROM AUTHOR]

Already installed cables are aging and the cable network is growing rapidly. Improved condition monitoring methods are required for greater visibility of insulation defects in the cable networks. One of the critical challenges for continuous monitoring is the large amount of partial discharge (PD) data that poses constraints on the diagnostic capabilities. This paper presents the performance comparison of two data acquisition techniques based on phase resolved partial discharge (PRPD) and pulse acquisition (PA). The major contribution of this work is to provide an in-depth understanding of these techniques considering the perspective of randomness of the PD mechanism and improvements in the reliability of diagnostics. Experimental study is performed on the medium voltage (MV) cables in the laboratory environment. It has been observed that PRPD based acquisition not only requires a significantly larger amount of data but is also susceptible to losing the important information especially when multiple PD sources are being investigated. On the other hand, the PA technique presents improved performance for PD diagnosis. Furthermore, the use of the PA technique enables the efficient practical implementation of the continuous PD monitoring by reducing the amount of data that is acquired by extracting useful signals and discarding the silent data intervals. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources, the low voltage distribution network (LVDN) is supposed to be the integrator of small distributed energy sources. This makes the users in LVDNs multifarious, which leads to more complex modeling. Additionally, data acquisition could be tricky due to rising privacy concerns. These impose severe demands on control schemes in LVDNs that the classical centralized control might not be able to fulfill. To tackle this, a model-free control approach with distributed decision-making architecture is proposed in this paper. Employing statistical methods and game theory, individual users in LVDNs achieve local optimum autonomously. Comparing to conventional approaches applied in LVDNs, the proposed approach is able to achieve active control with less communication burden and computational resources. The paper proves the convergence to the Nash Equilibrium (NE) and uses player compatible relations to form the specific equilibrium. A variant of the log-linear trial and error learning process is applied in a novel "suggest-convince" mechanism to implement the proposed approach. In the case study, a 103 nodes test network based on a real Belgian semiurban LVDN is illustrated. The proposed approach is validated and analyzed with practical load profiles on the 103 nodes network. In addition to that, centralized control is implemented as a benchmark to show the performance of the proposed approach by comparing it with the classical optimization result. The results demonstrate that the proposed approach is able to achieve player compatible equilibrium in an expected way, resulting in a good approximation to the local optimum. [ABSTRACT FROM AUTHOR]

Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and time commitments. Going beyond these two approaches, here, we propose a novel data acquisition protocol based on multiplexing appliances' signals to create an artificial database for energy disaggregation implementations tailored to each household and dedicated to performing under conditions of time and equipment constraints, requiring that only one smart meter be used and for less than a day. In a case study of a Greek household, we train and compare four common algorithms based on the data gathered through this protocol and perform two tests: an out-of-sample test in the artificially multiplexed signal, and an external test to predict the household's appliances' operation based on the time series of a real total consumption signal. We find accurate monitoring of the operation and the power consumption level of high-power appliances, while in low-power appliances the operation is still found to be followed accurately but is also associated with some incorrect triggers. These insights attest to the efficacy of the protocol and its ability to produce meaningful tips for changing energy behaviours even under constraints, while in said conditions, we also find that long short-term memory neural networks consistently outperform all other algorithms, with decision trees closely following. [ABSTRACT FROM AUTHOR]

Condition monitoring and overheating warnings of the main bearing of large-scale wind turbines (WT) plays an important role in enhancing their dependability and reducing operating and maintenance (O&M) costs. The temperature parameter of the main bearing is the key indicator to characterize the normal or abnormal operating condition. Therefore, forecasting the trend of temperature change is critical for overheating warnings. To achieve forecasting with high accuracy, this paper proposes a novel model for the WT main bearing, named stacked long-short-term memory with multi-layer perceptron (SLSTM-MLP) by utilizing supervisory control and data acquisition (SCADA) data. The model is mainly composed of multiple LSTM cells and a multi-layer perceptron regression layer. By combining condition parameters into a characteristic matrix, SLSTM can mine nonlinear, non-stationary dynamic feature relationships between temperature and its related variables. To evaluate the performance of the SLSTM-MLP model, experimental analysis was carried out from three aspects: different sample capacity sizes, different sampling time segments, and different sampling frequencies. Furthermore, the model's capability of online fault detection was also carried out by simulation. The results of comparative studies and online fault simulation tests show that the proposed SLSTM-MLP has better performance for temperature forecasting of the main bearing of large-scale WTs. [ABSTRACT FROM AUTHOR]

This paper demonstrates the investigation of the acquired outcomes from consistent information observing a 467.2 kWp solar photovoltaic (SPV) framework commissioned on the roofs of three separate high-rise buildings, which are located at the location of 26.9585° N and 80.9992° E. Onside real-time performance for this system was investigated for three years, 2018–2020; this system contains 1460 SPV panels of 320 Wp each, having 20 PV panels per string, 09 DC/AC power conditioning units (PCU), and a SCADA (supervisory control and data acquisition) system for monitoring the other necessary parts of a grid-interactive SPV system. The outcomes of the different buildings are compared with each other to analyze the power output at the same input conditions. Hardware components of the plants with approximately the same ratings (P2 ~ 108.8 kWp + P3 ~ 128 kWp) are compared (with P1 ~ 230.4 kWp). Simulation modeling of the year 2020 in PVsyst tool for generated energy, Performance Ratio (PR), and Capacity Utilization Factor (CUF) are carried out additionally and compared with the installed rooftop grid-interactive SPV system of 467.2 kWp (~P1 + P2 + P3) at the site. Numerous performance parameters such as array efficiency, inverter efficiency, system efficiency, Performance Ratio (PR), and Capacity Utilization Factor (CUF) of the plant are evaluated and compared with already installed systems in different regions of the world. These points demonstrate great feedback to framework architects, workers, designers, and energy suppliers regarding the genuine limit and plausibility of the framework they can offer to clients. Moreover, one of the environmental benefits of the SPV plant is that the 467.2 kWp PV framework reduces the tremendous measure of CO2, SO2, and NOX that is discharged into the air. [ABSTRACT FROM AUTHOR]

Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms. [ABSTRACT FROM AUTHOR]

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method. [ABSTRACT FROM AUTHOR]

As the coefficients of the Ångström–Prescott model is site-dependent, the sparsity of radiation stations in regions like the Tibetan Plateau (TP) presents challenges for model calibration. Due to the unique climate and the clean air conditions over the TP, it might be feasible to calibrate the Ångström–Prescott model with short-term observations from scientific expeditions. To test this hypothesis, we used various datasets with different lengths at four stations, together with 435 daily radiations measured during a scientific expedition at Banga in the central TP from 2014 to 2015, to calibrate the Ångström–Prescott model. We found that calibration with a 1-year data length resulted in model performances comparable to those with a 20-year data length. Analysis of the expedition observations showed that the monthly average daily radiation ranged from 15.2 MJ/m2d in December 2014 to 26.5 MJ/m2d in July 2015, with an average value of 20.6 MJ/m2d. When this set of expedition data was used for calibration, the Ångström–Prescott model performed well with an NSE (Nash–Sutcliffe efficiency) of 0.820. If no data were available for calibration, the coefficients of the Ångström–Prescott model could also be directly calculated by parameterization methods established with calibrations at the other radiation stations. In this situation, the LiuJD method performed the best with the highest NSE of 0.792, followed by the LiuXY method with an NSE of 0.764. The FAO method performed poorly with an NSE of 0.578, while the Gopinathan method performed the worst with the lowest NSE of 0.218. Thus, the best strategy to calibrate the Ångström–Prescott model in the Tibetan Plateau is to use data from local observations, even if collected over short periods. When these are not available, the coefficients of the Ångström–Prescott model should be calculated using the parameterization method established with calibrations over the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

In this paper, a Fractional Order Power System controller (FOPSS) is designed, and its performance and robustness are experimentally evaluated by tests in a 10 kVA laboratory scale power system. The FOPSS design methodology is based on the tuning of an additional design variable, namely the fractional order of the controller transfer function. This design variable is tuned aiming to obtain a tradeoff between satisfactory damping of dominant oscillating mode and improved closed-loop system robustness. For controller synthesis, transfer function models were estimated from data collected at selected operating points and subsequently applied for the controller design and for obtaining upper bounds estimates on the operating-point depends on plant uncertainties. The experimental results show that the FOPPS was able to obtain a robust performance for the considered set of the power system operating conditions. [ABSTRACT FROM AUTHOR]

This paper examines the data collected from the power industry over the last six years of actual reported emissions of sulphur hexafluoride (SF6) and the potential impact. The SF6 emissions have been collated from the 14 different regions in England, Scotland, and Wales (Great Britain) from the six distribution network operators. The emissions of SF6 due to the transmission network of Great Britain have also been collated from the three different transmission network operators. By collecting this SF6 emissions data from the power industry, in both the distribution and transmission networks, an overall view of the scale of SF6 emissions in Great Britain can be evaluated. Data from the power industry also shows the inventory of SF6 power equipment in use over the last six years in Great Britain and shows the calculated percentage leakage rate of all of this equipment. In this paper, these figures, as reported by the electrical power industry to the UK government, have been used to estimate the likely inventory of SF6 equipment in England, Scotland, and Wales by 2050 and the future emissions of SF6 that could be leaked into the atmosphere by this equipment. [ABSTRACT FROM AUTHOR]

This paper develops a novel energy universal service bus system (EUSBS) based on emerging energy Internet (E-net) technologies. This EUSBS is a unified identification and plug-and-play interface platform to which high penetration distributed energy and equipment (DEE), including photovoltaic (PV), fans, electric vehicle charging stations (EVCSs), energy storage equipment (ESE), and commercial and residential users (CRUs), can access in a coordinated control and optimized utilization mode. First, the functions design, overall framework and topology architecture design of the EUSBS are expounded, among which the EUSBS is mainly composed of a hardware system and a software platform. Moreover, several future application scenarios are presented. Then, the hardware part of EUSBS is designed and developed, including the framework design of this hardware subsystem, and development of the hardware equipment for PV access, fans access, EVCS access, ESE access, and CRU access. The hardware subsystem consists of smart socket, and household/floor/building concentrators. Based on this, the prototypes development of EUSBS hardware equipment is completely demonstrated. Third, the software part of the EUSBS is developed as a cloud service platform for electricity use data analysis of DEE. This software subsystem contains the power quality & energy efficiency analysis module, optimization control module, information and service module, and data monitoring and electricity behavior analysis module. Based on this design, the software interfaces are developed. Finally, an application study on energy management and optimization of a smart commercial building is conducted to evaluate the functions and practicality of this EUSBS. The EUSBS developed in this paper is able to overcome difficulties in big data collection and utilization on sides of distribution network and electricity utilization, and eventually implement a deep information-energy fusion and a friendly supply-demand interaction between the grid and users. This contribution presents a detailed and systematic development scheme of the EUSBS, and moreover, the laboratory prototypes of the hardware and software subsystems have been developed based on E-net technologies. This paper can provide some thoughts and suggestions for the research of active distribution network and comprehensive energy management and optimization in power systems, as well as references and guidance for researchers to carry out research regarding energy management, optimization and coordinated control of the smart buildings. [ABSTRACT FROM AUTHOR]

This article presents an automatic digital unbalanced impedance bridge for comparing two-port impedances: R-R, R-C and C-C. The bridge consists of a two-channel digital source of voltage sinusoidal waveforms supplying the bridge arms and a three-channel precise digitizer, by means of which the complex ratio of the source output voltages and the relative bridge unbalanced voltage are determined. The hardware implementation of the bridge is based on a relatively inexpensive universal data acquisition (DAQ) card by National Instruments, USB-6281, which contains, among others, a multi-channel 18-bit analog-to-digital converter (ADC) and two 16-bit digital-to-analog converters (DAC). Thanks to the new approach to the bridge operation algorithm, consisting in the use of the interpolation method in the comparison process, the influence of the accuracy of the measurement of the bridge unbalanced voltage and some bridge parasitic admittances on the uncertainty of the impedance comparison were minimized. This simplifies the measurement procedure and shortens its execution time. The obtained results confirmed that a relatively simple digital unbalanced bridge can compare impedance standards with an uncertainty of less than 10−5. By reducing the requirements regarding the uncertainty of the measurement, and thus its execution time, the presented bridge supplemented with impedance sensors can be used in many areas of industry. [ABSTRACT FROM AUTHOR]

The Digital Product Passport (DPP) is a concept of a policy instrument particularly pushed by policy circles to contribute to a circular economy. The preliminary design of the DPP is supposed to have product-related information compiled mainly by manufactures and, thus, to provide the basis for more circular products. Given the lack of scientific debate on the DPP, this study seeks to work out design options of the DPP and how these options might benefit stakeholders in a product's value chain. In so doing, we introduce the concept of the DPP and, then, describe the existing regime of regulated and voluntary product information tools focusing on the role of stakeholders. These initial results are reflected in an actor-centered analysis on potential advantages gained through the DPP. Data is generated through desk research and a stakeholder workshop. In particular, by having explored the role the DPP for different actors, we find substantial demand for further research on a variety of issues, for instance, on how to reduce red tape and increase incentives for manufacturers to deliver certain information and on how or through what data collection tool (e.g., database) relevant data can be compiled and how such data is provided to which stakeholder group. We call upon other researchers to close the research gaps explored in this paper also to provide better policy direction on the DPP. [ABSTRACT FROM AUTHOR]

This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability. [ABSTRACT FROM AUTHOR]

Air-quality measurements in a deep underground mine are a critical issue. The cost of ventilation, as well as the geometry of the considered mine, make this process very difficult, and local air quality may be a danger to miners. Thus, portable, personal devices are required to inform miners about gas hazards. There are available tools for that purpose; however, they do not allow the storage of data collected during a shift. Moreover, they do not allow the basic analysis of the acquired data cost-effectively. This paper aims to present a system using low-cost gas sensors and microcontrollers, and takes advantage of commonly used smartphones as a computing and visualization resource. Finally, we demonstrate monitoring system results from a test in an underground mine located in Poland. [ABSTRACT FROM AUTHOR]

The German Energiewende is a deliberate transformation of an established industrial economy towards a nearly CO2-free energy system accompanied by a phase out of nuclear energy. Its governance requires knowledge on how to steer the transition from the existing status quo to the target situation (transformation knowledge). The energy system is, however, a complex socio-technical system whose dynamics are influenced by behavioural and institutional aspects, which are badly represented by the dominant techno-economic scenario studies. In this paper, we therefore investigate and identify characteristics of model studies that make agent-based modelling supportive for the generation of transformation knowledge for the Energiewende. This is done by reflecting on the experiences gained from four different applications of agent-based models. In particular, we analyse whether the studies have improved our understanding of policies' impacts on the energy system, whether the knowledge derived is useful for practitioners, how valid understanding derived by the studies is, and whether the insights can be used beyond the initial case-studies. We conclude that agent-based modelling has a high potential to generate transformation knowledge, but that the design of projects in which the models are developed and used is of major importance to reap this potential. Well-informed and goal-oriented stakeholder involvement and a strong collaboration between data collection and model development are crucial. [ABSTRACT FROM AUTHOR]

At present, hybrid renewable power systems (HRPS) are considered reliable combinations for power generation under various conditions. The challenge facing researchers and engineers today is designing and implementing a reliable, efficient, and applicable SCADA system for adequate monitoring and control of hybrid power systems. In order to analyze, observe, and control the essential parameters of an HRPS, a SCADA system is crucial. As part of this study, a low-cost, low-power, open-source SCADA (Supervisory, Control, and Data Acquisition) system for hybrid renewable energy systems is presented. The system utilizes two remote terminal units (RTUs), an Arduino Mega2560 and a Wio terminal, to communicate with all actuators and measure vital system characteristics such as voltage, current, and power. Using the Firmata protocol, a laptop serves as the main terminal unit (MTU) to communicate with the Arduino. In addition to being the system's central component, Node-Red is utilized for processing, analyzing, storing, and displaying data. In contrast, a Wio terminal is used to display the values of all sensors in real-time on its LCD screen. As a whole, the proposed SCADA system is designed to keep the HRPS running smoothly and safely by displaying vital parameters, reporting any significant faults, and controlling the generator so that the batteries can be charged and discharged correctly. This article presents a complete description of all algorithms, experimental setups, testing, and results. [ABSTRACT FROM AUTHOR]

Recently, a lot of research has been carried out on partial discharge (PD) using machine learning techniques. However, most of these studies have focused on the identification of multiple PD sources, PD classification, or denoising PD measurements, with few studies on real-time PD occurrence detection. In this paper, we propose a method to detect PD occurrence based on anomaly pattern detection. The proposed method consists of three steps. First, in the data preprocessing step, the pulse sequence data are converted into a feature vector stream by applying a sliding window technique. In the next step, normal data modeling is performed using feature vectors transformed from pulse sequence data collected in a normal state where no PD occurs. Finally, for the monitored pulse sequence, an online process for PD detection is carried out through conversion to a feature vector data stream and an anomaly pattern detection method. Experimental results using simulated PD data demonstrate the capabilities of the proposed method. [ABSTRACT FROM AUTHOR]

The development of the road construction sector determines the consequences on consumption of non-renewable resources, energy expenditure and environmental pollution. Recent sustainability issues have highlighted the importance of efficient design and quality-oriented techniques in this sector, due to the huge amount of materials involved in construction and maintenance activities. Thus, it is necessary to properly quantify the environmental impacts of asphalt mixtures used for pavement construction, considering the whole life cycle of the products. Life cycle assessment (LCA) represents the most appropriate methodological framework for assessing the environmental burdens of a product, from raw material acquisition to final disposal. A common problem for LCA is the lack of primary data useful to calculate the product eco-profile, for a specific production process. In this context, there is generally limited reliable and accurate data regarding the asphalt plant production phase, which represents the most critical phase. Consequently, the aim of this paper is to perform an environmental/energy audit of an asphalt plant and, further, to collect and analyze primary data useful for the definition of the eco-profile of 1 metric ton of hot mix asphalt (HMA), following a "gate to gate" approach, including transport. The asphalt production is examined in a Sicilian batch-mix plant, representing one of the most commonly used for asphalt production in the Italian context. The results are of interest for asphalt mixture producers, contractors, transportation agencies and researchers seeking to quantify asphalt pavement environmental impacts in Italy, based on context-related foreground data. [ABSTRACT FROM AUTHOR]

Cities play a key role in driving the transition to sustainable energy. Urban areas represent between 60% and 80% of global energy consumption and are a significant source of CO2 emissions, making energy management at the urban scale an important area of research. Urban energy systems have a strong influence on the environment, economy, social dimensions and urban spatial planning. Energy consumption affects the urban microclimate, urban comfort, human health, and conversely, urban physical, economic and social characteristics affect the energy urban profile. In order to improve the quality of energy strategies, policies, and plans, local authorities need decision support tools, like energy potential mapping, which have risen significance in the last decades. Energy data are crucial for those tools. They can increase the quality and effectiveness of energy planning but also support the integration between energy and spatial planning. Energy data can also stimulate citizen engagement as well as encourage sustainable behaviours and CO2 emission reduction. This paper aims to increase the practice of data-aware planning, through the study of problems in energy data acquisition and processing observed in European projects focused on developing energy mapping tools. The problems observed attend to two main areas: technical and socio-economic issues. Those were derived from a comparison of energy mapping tools, and the work conducted for the PLANHEAT development. The scope of the research is to understand the main recurring issues in energy data acquisition and processing, in order to overcome the barriers in data availability. Increasing awareness of the relevance of energy data can foster the use of energy mapping tools, increasing the quality of energy policies and planning. [ABSTRACT FROM AUTHOR]

A businesses with a green label is associated with resources that are sustainable. This business is linked to the green economy, which can be described as a form of economy that is responsible in relation to the environment and economic growth, and thus complementary. In this type of economy, viable products are created, but also solutions and practices that take the environment into account. It is well known that eco-innovation activities are closely linked to the development of an eco-business. The research sample consisted of 10 countries, namely the Slovak Republic, the Czech Republic, Poland, Hungary, Austria, Germany, France, Italy, Sweden, and Finland, which were selected by purposive sampling. In this article, we look at eco innovations in selected countries, specifically ranking them, where we have divided countries into different levels, from countries that are at the super eco-innovation level, to countries that are in the middle zone, to countries that are significantly lagging in this trend. To classify countries into each level, we looked at the following variables: eco-innovation inputs, eco-innovation activities, environmental outcomes, socio-economic outcomes, and the eco-innovation index itself. Taking these sub-results into account, we determined where countries are, in other words, which level they have reached. We found that there are significant differences between countries. As we conclude, there are several reasons for this, but one of them is the lack of communication, coordination, and synergy between institutions, government, and SMEs, which are the drivers of eco-innovation. The supporting quantitative data collection method was data collection and structured observation, which is more precise and therefore provides more detailed information about the reality under study. For the purpose of this thesis, data were obtained from the Statistical Office of the European Union, that is, Eurostat, which is responsible for publishing pan-European statistics and indicators that allowed us to compare countries. In the survey, we compared the five most recent years for which Eurostat data were available, namely 2017, 2018, 2019, 2020, and 2021. The overall score of a European Union member state is calculated as the unweighted average of 16 sub-indicators. It shows how well each Member State performs in terms of eco-innovation compared to the European Union average of 100. [ABSTRACT FROM AUTHOR]

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance. [ABSTRACT FROM AUTHOR]