Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based... [more]

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October−May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predi... [more]

This study aims to evaluate the effects of price liberalization on energy consumption intensity, because price liberalization leads to improved productivity, energy consumption management, and consumption reform. Although the energy consumption of large-scale factories and industries has increased dramatically, and the energies losses have an increment in the power transmission lines, this policy can result in decreasing the energy consumption intensity due to the changes in consumption patterns. If there is a correlation between two variables, the price can be a valid variable to control cost and increase consumption efficiency. The augmented Dickey−Fuller (ADF) and the Chi-squared tests are also employed to investigate the maneuverability of these variables in the first-order contrast. In this case study, the energy consumption intensity response to price changes using the data gathered between 1988−2020, has gained a confidence interval of these reactions at 95%. The proposed vector... [more]

Adsorption cooling technologies driven by low-grade thermal or solar power are used as an energy-efficient alternative to conventional refrigeration and air conditioning systems. Explicit understanding of the adsorption cycles requires precise determination of the performance parameters, replication of the experimental data, and the rigorous study of the adsorption heat transformation method. Hence, the optimum adsorption isotherms model must be identified. Scientists often face difficulties in selecting the suitable isotherm model as there are many models for a particular form of adsorption isotherm. The present study introduces a novel approach for choosing the optimal models for each type of International Union of Pure and Applied Chemistry (IUPAC) classified adsorption isotherm using robust statistical methods. First, the box-and-whisker plots of error identification are employed. Tóth for Type-I(a) and Type-I(b), modified BET for Type-II, GAB for Type-III, Universal for Type-IV(a)... [more]

The COVID-19 pandemic has impacted electricity consumption patterns and such an impact cannot be analyzed by simple data analytics. In China, specifically, city lock-down policies lasted for only a few weeks and the spread of COVID-19 was quickly under control. This has made it challenging to analyze the hidden impact of COVID-19 on electricity consumption. This paper targets the electricity consumption of a group of regions in China and proposes a new clustering-based method to quantitatively investigate the impact of COVID-19 on the industrial-driven electricity consumption pattern. This method performs K-means clustering on time-series electricity consumption data of multiple regions and uses quantitative metrics, including clustering evaluation metrics and dynamic time warping, to quantify the impact and pattern changes. The proposed method is applied to the two-year daily electricity consumption data of 87 regions of Zhejiang province, China, and quantitively confirms COVID-19 has... [more]

Energy is the main driver of human Social-Ecological System (SES) dynamics. Collective energy properties of human SES can be described applying the principles of statistical mechanics: (i) energy consumption repartition; (ii) efficiency; (iii) performance, as efficient power, in relation to the least-action principle. International Energy Agency data are analyzed through the lens of such principles. Declining physical efficiency and growth of power losses emerge from our analysis. Losses mainly depend on intermediate system outputs and non-energy final output. Energy performance at Country level also depends on efficient power consumption. Better and worse performing Countries are identified accordingly. Five policy-relevant areas are identified in relation to the physical principles introduced in this paper: Improve efficiency; Decouple economic growth from environmental degradation; Focus on high value added and labor-intensive sectors; Rationalize inefficient fossil fuel subsidies t... [more]

This paper presents a method for shaft position estimation of a synchronous motor with permanent magnets. Zero speed and very low speed range are considered. The method uses the analysis of high-frequency currents induced by the introduction of additional voltage in the control path in the stationary coordinate system associated with the stator. An artificial neural network estimates the sine and cosine values necessary in the Park’s transformation units. This method can achieve satisfactory accuracy in the case of low asymmetry of inductance in the direct and quadrature axes of the coordinate system associated with the rotor. The TensorFlow/Keras package was used for artificial network calculations and the scikit-learn package for preprocessing. Aggregating the outputs of several artificial neural networks provides an opportunity to reduce the resultant estimation error. The use of as few as four networks has enabled the error to be reduced by approximately 20% compared to a single ex... [more]

This paper addresses the damage behaviour of a nickel/yttria-stabilised zirconia (Ni-YSZ) anode, in order to understand microstructural degradation processes of Solid Oxide Fuel Cells (SOFCs) during long-term operation. Numerical investigations are carried out to analyse the failure mechanisms in detail. For this purpose, finite element (FE) models are generated from focused ion beam-scanning electron microscopy 3D image data, representing the anode microstructure with varying phase compositions. A brittle model and a ductile material model were assigned to the YSZ phase and the nickel phase, respectively. The porosity is found to affect the strength of the microstructure significantly, leading to low compressive strength results. A high Ni content generally increases the toughness of the overall structure. However, the orientation and the geometry of the nickel phase is essential. When the Ni phase is aligned parallel to the loading direction, a supporting effect on the microstructure... [more]

This research seeks to identify non-financial enterprises exposed to the climate risk relating to transition risks and at the same time use of bank loans, as well as to conduct stress tests to take account of the financial risk related to climate change. The workflow through which to determine the ability of the banking sector to assess the potential impact of climate risk entails parts based around economic sector and company level. The procedure based on the sectoral level identifies vulnerable economic sectors (in the Sectoral Module), while the procedure based on company level (the Company Module) refers to scenarios presented in stress tests to estimate the probability of default under stressful conditions related to the introduction of a direct carbon tax. The introduction of the average direct carbon tax (EUR 75/tCO2) in fact results in increased expenditure and reduced sales revenues among enterprises from sectors with a high CO2 impact, with the result being a decrease in the... [more]

The article proposes the use of the analytic hierarchy process (AHP) method to select a risk assessment technique associated with underground hydrogen storage. The initial choosing and ranking of risk assessment techniques can be considered as a multi-criteria decision problem. The usage of a decision model based on six criteria is proposed. A ranking of methods for estimating the risks associated with underground hydrogen storage is presented. The obtained results show that the application of the AHP-based approach may be a useful tool for selecting the UHS risk assessment technique. The proposed method makes it possible to make an objective decision of the most satisfactory approach, from the point of view of all the adopted decision criteria, regarding the selection of the best risk assessment technique.

The challenge of maintaining the required level of mobility and air quality in cities can be met by deploying an appropriate management system in which the immediate vicinity of roads is monitored to identify potential pollution hotspots. This paper presents an integrated low-cost system which can be used to study the impact of traffic related emission on air quality at intersections. The system was used for three months in 2017 at five locations covering intersections in the centre of a mid-sized city. Depending on the location, pollution hotspots with high PM2.5 and PM10 concentrations occurred 5−10% of the time. It was shown that despite the close mutual proximity of the locations, traffic and the immediate surroundings lead to significant variation in air quality. At locations with adverse ventilation conditions a tendency towards more frequent occurrences of moderate and sufficient air quality was observed than at other locations (even those with more traffic). Based on the result... [more]

Ongoing climate change and increasing energy consumption are becoming a serious threat to international development efforts. To ensure a secure and sustainable future, local, national and regional authorities, as well as energy companies, need to be involved in improving energy efficiency and promoting rational energy use. The consumer is always at the centre of interest of policy and energy reduction strategies makers. It is their behaviour, motives and beliefs that can lead to optimised and economical energy management. This paper responds to the need to identify the individual preferences of energy consumers. The presented research fills an existing gap in the literature by analysing the strength of the influence of different types of instruments and external stimuli shaping pro-environmental attitudes of consumers of different behavioural types. The analysis presented in this paper is the result of the next stage of the authors’ research on energy consumers modelling, their segment... [more]

Fixed and adaptive supervised dictionary learning (SDL) is proposed in this paper for wide-area stability assessment. Single and hybrid fixed structures are developed based on impulse dictionary (ID), discrete Haar transform (DHT), discrete cosine transform (DCT), discrete sine transform (DST), and discrete wavelet transform (DWT) for sparse features extraction and online transient stability prediction. The fixed structures performance is compared with that obtained from transient K-singular value decomposition (TK-SVD) implemented while adding a stability status term to the optimization problem. Stable and unstable dictionary learning are designed based on datasets recorded by simulating thousands of contingencies with varying faults, load, and generator switching on the IEEE 68-bus test system. This separate supervised learning of stable and unstable scenarios allows determining root mean square error (RMSE), useful for online stability status assessment of new scenarios. With respec... [more]

This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that is using this innovative system, the algorithm automatically forecasts the number of SOs that will need to be executed in each location in several time steps (hourly, monthly and yearly basis). The forecasted emergency SOs demand, which is related to energy outages, are stochastically distributed, projecting the impacted consumers and its individual interruption indexes. This spatio-temporal forecasting is the main input for a web-based platform for optimal bases allocation, field team sizing and scheduling implemented in the eleven distribution utilities of Energisa group in Brazil.

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patt... [more]

The subject of research is a phenomenon of prosumption, i.e., the intertwining of consumption and production processes, until the differences between them are blurred. When consumers produce goods and services for their use, they become prosumers. The article aimed to assess consumer behavior in terms of various forms of prosumer activity on the market of household-related services. The types of prosumer activity of rural households and their size were determined during the analyses. The article uses primary sources from a survey conducted among residents of rural areas of the Masovian Voivodeship in Poland in 2017. Statistical, descriptive, and comparative methods were used. The research shows that consumers are very active in the field of prosumption, rationally running their households. The study used principal component analysis (PCA) and selected descriptive statistics. The research results showed that the services performed can be grouped into three categories, i.e., the index of... [more]

Building extraction from remote sensing (RS) images is a fundamental task for geospatial applications, aiming to obtain morphology, location, and other information about buildings from RS images, which is significant for geographic monitoring and construction of human activity areas. In recent years, deep learning (DL) technology has made remarkable progress and breakthroughs in the field of RS and also become a central and state-of-the-art method for building extraction. This paper provides an overview over the developed DL-based building extraction methods from RS images. Firstly, we describe the DL technologies of this field as well as the loss function over semantic segmentation. Next, a description of important publicly available datasets and evaluation metrics directly related to the problem follows. Then, the main DL methods are reviewed, highlighting contributions and significance in the field. After that, comparative results on several publicly available datasets are given for... [more]

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, novel hybrid machine learning approaches that exploit auxiliary numerical data are proposed. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reductio... [more]

Two different concepts of wave energy converter coupled to the novel C-GEN linear generator have been studied numerically, including the evaluation of different buoy sizes. The first concept has a slack connection between the buoy and the generator on the seabed. Another concept is based on a stiff connection between the buoy and the generator placed on an offshore platform. Three different approaches to calculate the damping force have been utilized within this study: the optimal damping coefficient, R-load, and RC-load. R-load is a model for the load applied to a grid-connected generator with passive rectification. RC-load is a model for a phase angle compensation applied to a system with active rectification. The radiation forces originating from the oscillatory motion of the buoy have been approximated using the transfer function in the frequency domain and the vector fitting algorithm. A comparison of the approximation methods is presented, and their accuracy has been evaluated. T... [more]

Debris flows are capable of flowing with high velocities and causing significant economic and infrastructural damage. As a hazard mitigation measure, physical barriers are frequently installed to dissipate the energy of debris flows. However, there is a lack of understanding on how barriers affect and interact with debris-flow behavior (e.g., velocity and volume). This study investigated the changes in debris-flow characteristics depending on the installation location of barriers. Mt. Woomyeon, which is located in Seoul, Korea, was the site of a major debris-flow event in 2011. This study modeled this event using DAN3D, numerical software based on smoothed particle hydrodynamics (SPH). Our numerical approach assessed changes in debris-flow behavior, including velocity and volume, as the debris flow interacts with four closed-type barriers installed at separate points along the flow path. We used DAN3D to model the barriers via terrain elevation modifications. The presence of a closed-t... [more]

To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and i... [more]

European Union Allowances (EUAs) are rights to emit CO2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). Th... [more]

This article is concerned with the numerical solution of nonlinear hyperbolic Schro¨dinger equations (NHSEs) via an efficient Haar wavelet collocation method (HWCM). The time derivative is approximated in the governing equations by the central difference scheme, while the space derivatives are replaced by finite Haar series, which transform it to full algebraic form. The experimental rate of convergence follows the theoretical statements of convergence and the conservation laws of energy and mass are also presented, which strengthens the proposed method to be convergent and conservative. The Haar wavelets based on numerical results for solitary wave shape of |φ| are discussed in detail. The proposed approach provides a fast convergent approximation to the NHSEs. The reliability and efficiency of the method are illustrated by computing the maximum error norm and the experimental rate of convergence for different problems. Comparisons are performed with various existing methods in re... [more]

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle... [more]

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) log... [more]