Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum−Sandras, and Bimodal−Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving com... [more]

Legal requirements for the use of refrigerants increasingly restrict the use of high-global warming potential (GWP) refrigerants. As a result, there is a growing interest in natural refrigerants and in those belonging to the hydrofluoroolefins (HFO) class, which can be used on their own or in mixtures. One of them is the R1234ze(E) refrigerant, an alternative to the R134a refrigerant as well as being a component of numerous mixtures. The knowledge of thermodynamic and transport properties of refrigerants is required for the analysis and calculation of refrigeration cycles in refrigeration, air conditioning, or heating systems. The paper presents analytical equations for calculating the properties of the R1234ze(E) refrigerant in the state of saturation and in the subcooled liquid and superheated vapour regions that do not require numerical calculations and are characterised by small deviations. The Levenberg−Marquardt algorithm—one of the methods for non-linear least squares estimation... [more]

Energy consumption in the building sector has been continuously increasing, and energy conservation in this sector has become critical for achieving the national goal of reducing greenhouse gas emissions. In South Korea, information on energy sources (electricity, gas, district heating, etc.) is provided, but detailed energy use information, such as space heating, space cooling, domestic hot water (DHW) and lighting, is insufficient to establish a specific action plan for energy savings. Energy use information by end-use can be acquired through actual measurements in close proximity to actual energy use. This information reflects the effects of complex elements such as building operations and residential characteristics, but it is also insufficient. This study presents statistical data on energy use intensities (EUIs) and greenhouse-gas-emission intensities by end-use, derived by measured data collected from 48 sample office buildings in representative city from May 2017 to April 2018,... [more]

Understanding and integrating the user’s decision-making process into product design and distribution strategies is likely to lead to higher adoption rates and ultimately increased impacts, particularly for those products that require a change in habit or behavior such as clean energy technologies. This study applies the Theory of Planned Behavior (TPB) in design for global development, where understanding the tendency to adopt beneficial technologies based on parsimonious approaches is critical to programmatic impact. To investigate robustness and applicability of behavioral models in a data scarce setting, this study applies TPB to the adoption of biomass cookstoves in a sample size of two remote communities in Honduras and Uganda before and after a trial period. Using multiple ordinal logistic regressions, the intention to adopt the technology was modeled. Results quantify the influence of these factors on households’ intentions to cook their main meals with improved cookstoves. For... [more]

In relation to the situation caused by the pandemic, which may also take place in the future, there is a need to find effective solutions to improve the economic situation of the floristry industry. The production and sale of flowers is time-consuming and long-term. Therefore, any information that causes the impossibility of selling the plants will result in a reduction of profitability or bankruptcy of such companies. Research on rationally utilizing biowaste from plant cultivation as well as unsold flowers for environmental protection and effective use of their potential as a raw material for bioenergy production were examined in this article. The aim of this study was to analyze the energetic potential of the biodegradable fraction of waste from floriculture. The trials included floricultural waste containing the stems, leaves and flowers of different species and hybrid tulips (Tulipa L.), roses (Rosa L.), sunflowers (Helianthus L.) and chrysanthemums (Dendranthema Des Moul.). Their... [more]

The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained res... [more]

Subscale wind turbines can be installed in the field for the development of wind technologies, for which the blade aerodynamics can be designed in a way similar to that of a full-scale wind turbine. However, it is not clear whether the wake of a subscale turbine, which is located closer to the ground and faces different incoming turbulence, is also similar to that of a full-scale wind turbine. In this work we investigate the wakes from a full-scale wind turbine of rotor diameter 80 m and a subscale wind turbine of rotor diameter of 27 m using large-eddy simulation with the turbine blades and nacelle modeled using actuator surface models. The blade aerodynamics of the two turbines are the same. In the simulations, the two turbines also face the same turbulent boundary inflows. The computed results show differences between the two turbines for both velocity deficits and turbine-added turbulence kinetic energy. Such differences are further analyzed by examining the mean kinetic energy equ... [more]

The water-supply system is one of the basic and most important critical infrastructures. Water supply service disruption (water quality or quantity) may have serious consequences in modern societies. Water supply service is subject to various failure modes. Failure modes are specified by their degradation mechanisms, criticality, occurrence frequency and intensity. These failure modes have a random nature that impacts on the network disruption indicators, such as disruption frequency, network downtime, network repair time and network back-to-service time, i.e., the network resilience. This paper focuses on the water leakage failure mode. The water leakage failure mode assessment considers the unavoidable annual real water losses and the infrastructure leakage index recommended by the International Water Association’s Water Loss Task Force specialist group. Probabilistic statistical modelling was implemented to assess the seasonal index, the failure rates and the expectation value of th... [more]

The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accurac... [more]

Standard ISO 10077-2 gives the procedure to calculate thermal transmittances of window frames in 2D numerical simulations. It also introduces some examples of frame geometrical models with all necessary input data and the solutions so as to perform validation of the applied numerical tools. In the present paper, the models prepared with a commercial finite volume software of a PVC window frame were first positively validated with the results given in the Standard. An experimental test was then implemented to confirm the simulated data, with satisfactory agreement. The numerical code was used on one of the frames provided by the Standard to perform a sensitivity analysis of all the components and boundary conditions playing a role on the definition of the frame thermal transmittance, such as surface heat transfer coefficients, values of the solid thermal conductivity, emissivity and insulation properties of air gaps. Results demonstrate that the air gap properties represent the most inf... [more]

This work proposes a method to distinguish between various flow patterns in a multiphase gas−liquid system. The complete discrimination between different flow patterns can be achieved by mapping the corresponding frequency and statistical parameters. These parameters are usually obtained from further analysis conducted on the signal data of the utilized sensor. The proposed technique is based on establishing interrelationships between these parameters, namely the mean (m), the standard deviation ( σ ¯ ), power spectral density (PSD), the width of the characteristic frequency peaks (Δƒ), the skewness ( γ 1 ) and the kurtosis ( γ 2 ). Therefore, a relatively simple electrical capacitance sensor with two electrodes was designed and implemented on a two-phase flow apparatus with a circular pipe. The experimental operating conditions comprised of different combinations of air−water superficial velocities at three inclinations (i.e., horizontal, upward 15° and upward 30... [more]

Because permanent-magnet synchronous linear motors (SLM) still exhibit nonlinear friction, ending effects and time-varying dynamic uncertainties, better control performances cannot be achieved by using common linear controllers. We propose a backstepping approach with three adaptive laws and a beating function to control the motion of permanent-magnet SLM drive systems that enhance the robustness of the system. In order to reduce greater vibration in situations with uncertainty actions in the aforementioned control systems, we propose an adaptive modified recurrent Rogers−Szego polynomials neural network backstepping (AMRRSPNNB) control system with three adaptive laws and reimbursed controller with decorated gray wolf optimization (DGWO), in order to handle external bunched force uncertainty, including nonlinear friction, ending effects and time-varying dynamic uncertainties, as well as to reimburse the minimal rebuild error of the reckoned law. In accordance with the Lyapunov stabilit... [more]

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to... [more]

Response surface methodology was used to derive a prediction model for organic acids production by anaerobic acidogenic fermentation of cellobiose, using a mixed culture immobilized on γ-alumina. Three parameters (substrate concentration, temperature, and initial pH) were evaluated. In order to determine the limits of the parameters, preliminary experiments at 37 °C were conducted using substrates of various cellobiose concentrations and pH values. Cellobiose was used as a model sugar for subsequent experiments with lignocellulosic biomass. The culture was well adapted to cellobiose by successive subculturing at 37 °C in synthetic media (with 100:5:1 COD:N:P ratio). The experimental data of successive batch fermentations were fitted into a polynomial model for the total organic acids concentration in order to derive a predictive model that could be utilized as a tool to predict fermentation results when lignocellulosic biomass is used as a substrate. The quadratic effect of temperature... [more]

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term m... [more]

The superimposed transient electric field generated by polarity reversal causes severe stress to the high-voltage direct current (HVDC) cable insulation. Especially for polymeric insulation materials, space charge accumulation is prominent, which strengthens local electric field intensity. In order to avoid the risk of dielectric breakdown resulting from an intensified electric field caused by space charge behavior, several numerical analyses have been conducted using the Bipolar Charge Transport (BCT) model. However, these studies have only considered a unidirectional electric field assuming only steady state operating conditions, and there are few works that have analyzed space charge behavior during transient states, especially for the polarity reversal period. In order to analyze the charge behavior under polarity reversal, it is necessary to establish the boundary condition considering the direction and intensity of the field. Therefore, in this paper, we proposed a modified model... [more]

In this article, an investigation of a free-piston Stirling engine-based micro-cogeneration (μCHP) unit is presented. This work is a step towards making the system calculations more reliable, based on a data-driven model, which enables the time-domain simulation of the μCHP behavior. A laboratory setup was developed that allowed for the measurement of a micro-cogeneration unit during long-term operation with a variable thermal load. The maximum efficiency of electricity generation was equal to 13.2% and the highest overall efficiency was equal to 95.7%. A model of the analyzed μCHP system was developed and validated. The simulation model was based on the device’s characteristics that were obtained from the measurements; it enables time-domain calculations, taking into account the different operating modes of the device. The validation of the system showed satisfactory compliance of the model with the measurements: for the period modeled of 24 h, the error in the heat generation fluctua... [more]

Although there are many studies available in literature about biomass pyrolysis or devolatilization using thermogravimetric analysis (TGA), the effects of important operating parameters have infrequently been investigated for pine wood particle combustion. Consequently, the present study investigates the influence of particle size (63 µm to 1 mm), heating rate (5 to 243 °C/min), and air flow rate (10 to 150 mL/min) on the mass loss of pine wood using TGA. Additionally, the kinetic parameters considering the different conditions were determined to be incorporated in a numerical model. The effect of the heating rate on the thermal decomposition behavior has shown that the thermogravimetric and derivative thermogravimetric curves were shifted to higher temperatures with the increase in the heating rate. In this way, the heating rate affects the temperature at which the highest mass loss rate occurs as well as its value. Furthermore, comparing the higher and lower heating rate, the time to... [more]

This paper presents the analysis of power supply restoration time after failures occurring in power lines. It found that the power supply restoration time depends on several constituents, such as the time for obtaining information on failures, the time for information recognition, the time to repair failures, and the time for connection harmonization. All these constituents have been considered more specifically. The main constituents’ results values of the power supply restoration time were analyzed for the electrical networks of regional power supply company “Oreolenergo”, a branch of Interregional Distribution Grid Company (IDGC) of Center. The Delphi method was used for determining the time for obtaining information on failures as well as the time for information recognition. The method of mathematical statistics was used to determine the repair time. The determined power supply restoration time (5.28 h) is similar to statistical values of the examined power supply company (the dev... [more]

Mostly, it is very difficult to obtained the exact solution of fractional-order partial differential equations. However, semi-analytical or numerical methods are considered to be an alternative to handle the solutions of such complicated problems. To extend this idea, we used semi-analytical procedures which are mixtures of Laplace transform, Shehu transform and Homotopy perturbation techniques to solve certain systems with Caputo derivative differential equations. The effectiveness of the present technique is justified by taking some examples. The graphical representation of the obtained results have confirmed the significant association between the actual and derived solutions. It is also shown that the suggested method provides a higher rate of convergence with a very small number of calculations. The problems with derivatives of fractional-order are also solved by using the present method. The convergence behavior of the fractional-order solutions to an integer-order solution is ob... [more]

Accurate measurement of air flow rate is essential in automatic building control using the variable air volume (VAV) system. In order to solve the problems of the existing air flow measurement method and improve the accuracy of air flow control, this study developed a data-based multiple regression air flow prediction model. The independent variables used in the development of the predictive model were selected as the factors used for control and monitoring when operating with variable air flow rate in the existing air conditioning system. Data collection and correlation between independent variables and air flow rate of the terminal unit were analyzed. Using the IBM SPSS statistics version 25, an air flow rate prediction model was developed using multiple regression analysis. Reliability of model was evaluated by comparing the measured airflow. The relative error of −9.3% to 10.4% is shown when comparing the estimated air flow rate by the developed model with the measured air flow rat... [more]

Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates multiple sources of information by extracting features from (i) contextual data (weather, calendar), and (ii) the historical load of the particular household and all households present in the dataset. Additionally, we compute novel domain-specific time-series features that allow the system to better model the pattern of energy consumption of the household. The experimental analysis and evaluation were performed on one of the most extensive datasets for household electrical energy consumption, Pecan Street, containing almost four years of data. Multiple test cases show that the proposed model provides... [more]

Ukraine has enough biomass resources for biogas production. However, this energy potential is not used sufficiently. This research is aimed at examining the current experience of commercial biogas systems in the Europe Union and its adaptation for Ukraine. Special attention was paid to economic indicators, such as specific investment costs, production costs (biogas, biomethane, and electricity), and incentives. Using statistical data for the European Union and Ukraine, the biogas potential for Ukraine (based on European experience) was determined. The economic competitiveness of biogas production was evaluated compared to alternatives, such as photovoltaic, wind power, biomass, conventional fuels, and liquid biofuels. The results showed that biogas complexes have higher specific investment costs and produce more expensive electricity. It was highlighted that agricultural residues and industrial waste are sustainable feedstocks for biogas systems. A perspective biogas plant is a plant t... [more]

Accurate forecasting of demand load is momentous for the efficient economic dispatch of generating units with enormous economic and reliability implications. However, with the high integration levels of grid-tie generations, the precariousness in demand load forecasts is unreliable. This paper proposes a data-driven stochastic ensemble model framework for short-term and long-term demand load forecasts. Our proposed framework reduces uncertainties in the load forecast by fusing homogenous models that capture the dynamics in load state characteristics and exploit model diversities for accurate prediction. The ensemble model caters for factors such as meteorological and exogenous variables that affect load prediction accuracy with adaptable, scalable algorithms that consider weather conditions, load features, and state characteristics of the load. We defined a heuristic trained combiner model and an error correction model to estimate the contributions and compensate for forecast errors of... [more]

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement a... [more]