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Printed Edition of the Special Issue Published in Applied Sciences

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This means that the western side of the sea still remains its most energetic part. Furthermore, a general increase in wave energy is observed, and it appears that this increase is relatively higher on the western side.

Sustainable Energy Systems of the Future

Analysis of heat transfer and thermal environment in a rural residential building for addressing energy poverty.Appl. A new method based on neural networks for the design of internal coatings in buildings: energy saving and thermal comfort.Appl.

Study of the Wave Energy Propagation Patterns in the Western Black Sea

  • Introduction
  • Materials and Methods 1. Theory of SWAN Spectral Model
  • Results and Discussion
  • Conclusions

From this point of view, Figure 4 shows the conditions of high waves in the entire Black Sea basin (computational domain Sph1). Assessment of wave energy in the Black Sea based on a 15-year display with data assimilation.

Table 1. Characteristics of the computational domains defined in spherical coordinates for the Simulating Waves Nearshore (SWAN) model simulations focused on the western side of the Black Sea.
Table 1. Characteristics of the computational domains defined in spherical coordinates for the Simulating Waves Nearshore (SWAN) model simulations focused on the western side of the Black Sea.

A Hybrid Fuzzy Analysis Network Process (FANP) and the Technique for Order of Preference by

Similarity to Ideal Solution (TOPSIS) Approaches for Solid Waste to Energy Plant Location Selection

Material and Methodology 1. Research Development

The FANP model is the most efficient tool to define the weight of the criteria. If CR≤0.1 is satisfactory, unlike CR≥0.1 then we need to perform a re-evaluation of the pairwise comparison matrix.

Figure 3. Research methodologies.
Figure 3. Research methodologies.

Results and Discussion

In Figure 8, Hau Giang (DMU 8) has the shortest geometric distance from the PIS and the longest geometric distance from the NIS. The results show that DMU 8 (Hau Giang) is the best place for building a solid waste-to-energy plant in Vietnam.

Conclusions

Moreover, there is no research using the solid waste MCDM model for energy plant location selection in Vietnam. This is a reason why we proposed a hybrid fuzzy analysis network process (FANP) and the technique of preference order by similarity to the ideal solution (TOPSIS) for solid waste for energy plant location selection in Vietnam.

Table A1. Comparison matrix for ECF.
Table A1. Comparison matrix for ECF.

Smart System for the Optimization of Logistics Performance of the Pruning Biomass Value Chain

Components of Smart Logistics System (SLS) Prototype

Based on the Cargolog Impact Recorder System platform, the Smartbox is a measurement unit that records pruning biomass temperature and humidity (using the sensor probe), QR measurements (using a scanner) and Global Positioning System (GPS) coordinates (using GPS signal receivers) during biomass transport. The functions of the Smartbox are programmed using the CargoLog PC software and linked to the web address of the Information Platform [20].

Figure 2. The conceptual organization of smart logistics system (SLS) components (Adapted from [17]
Figure 2. The conceptual organization of smart logistics system (SLS) components (Adapted from [17]

Implementation and Management of the SLS 1. Material Flow Along the Logistics Chain

The central control unit was designed to allow the administrator of the cropping trading system to monitor the operation of the SLS as a whole. The collected data will be stored on the platform server and controlled by the system administrator. Quantification of residual biomass obtained from vineyard pruning in the Mediterranean region. Bioenergy of biomass.

Quantification of residual biomass obtained from tree pruning in Mediterranean olive groves. Biomass Bioenergy.

Table 3. Data type, source, destination and utilization.
Table 3. Data type, source, destination and utilization.

Evaluation of a Smart System for the Optimization of Logistics Performance of a Pruning Biomass

Evaluation Results and Discussion

In this section, the product quality model is used to analyze the performance of the intelligent system. Functionality: The components of the smart box are connected to a recording unit by means of cables [2] (see Figure 5). Functional features of the smart box and centralized information platform were taken into account during testing.

Smart system for optimizing logistics performance of the pruning biomass value chain.Appl.

Table 5 indicates that, out of the recorded 104 products, route planning was tested for 54%, while actual product delivery of 16% was confirmed according to the data captured by the smart system.
Table 5 indicates that, out of the recorded 104 products, route planning was tested for 54%, while actual product delivery of 16% was confirmed according to the data captured by the smart system.

A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble

Search Algorithm

Materials and Methods

Wind speed series from four datasets are chosen to test the prediction accuracy of the proposed hybrid model. The validity of the proposed hybrid model was analyzed based on the results of multistep forecasting. The prediction results of the proposed model and conventional models are shown in Tables 4 and 5.

This paper uses four wind turbines to perform the wind speed prediction experiment.

Figure 1. The flowchart of the Cuckoo Search Algorithm.
Figure 1. The flowchart of the Cuckoo Search Algorithm.

Discussion

One-step forecasting results of the proposed model and other traditional models (BP RBF ARIMA). Two-step forecasting results of the proposed model and other traditional models (BP RBF ARIMA). Three-step forecasting results of the proposed model and other traditional models (BP RBF ARIMA).

In Table 8, the forecast performance of the proposed model outperforms all the other models in both first order and second order.

Figure 5. One-step forecasting results of the proposed model and other traditional models (BP RBF ARIMA).
Figure 5. One-step forecasting results of the proposed model and other traditional models (BP RBF ARIMA).

Conclusions

Short-term wind speed forecasting using wavelet transform and genetic algorithm-optimized support vector machines. Restore. Noise model-based v-support vector regression with its application to short-term wind speed forecasting. Neural Netw. Very Short Term Wind Speed ​​Forecasting: A New Artificial Neural Network-Markov Chain Model. Energy conversions.

Short-term wind speed forecasting in wind farms based on banks of support vector machines.Wind energy.

Smart-Grid-Aware Load Regulation of Multiple Datacenters towards the Variable Generation of

Background and Related Work

The refrigerator could automatically change its temperature as the grid frequency changed, which meant changing the load power. In this paper, we focus on the interaction of data centers and renewable power plants in the smart grid transmission network. In particular, equation (5) is the constraint on the bus voltage of each node and equation (6) gives the constraint on the power flow in each branch.

An imbalance between load and generation can lead to the failure of the normal operation of the electricity grid.

Figure 1. Model of the IEEE 30-bus test system.
Figure 1. Model of the IEEE 30-bus test system.

Experiment Results and Analysis 1. Testbed Setup and Parameter Settings

We specify that if the power supply to the data center is out during the next interval. The effect of different operation delay times on the total power losses in the network can be seen in Figure 6. From the figure, we can see that the value of the loss increases proportionally with the delay, which has a great impact on the action delay on the power loss of the entire network.

Figure 8 shows the prediction results at a 10-minute data center action delay, with the average prediction error in Figures 8a,c being 5% and 10%, respectively.

Figure 3. Input solar generation power. (a) Sunny day; (b) cloudy/snowy day.
Figure 3. Input solar generation power. (a) Sunny day; (b) cloudy/snowy day.

Conclusions and Future Work

In Proceedings of the 2nd International Conference on Computer Science and Artificial Intelligence 2018 (CSAI 2018), Shenzhen, China, 8–10 December 2018. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, Banff10, AB20, Banff10, AB, June 6, 10, 2017 IEEE International Conference on Electrical Engineering , Electronics and Optimization (ICEEOT), Chennai, India, 3–5 March 2016; page

In Proceedings of the IEEE Power Systems Conference and Exhibition, New York, NY, USA, 10–13 October 2004.

Meteorological Variables’ Influence on Electric Power Generation for Photovoltaic Systems Located at

Different Geographical Zones in Mexico

Methodology 1. Statistical Method

The correlation analysis is one of the most widely used and reported statistical methods for scientific and medical research; its visual representation is known as a dispersion graph. The coefficient of determination R2 is defined as the percentage of the variation of the values ​​of the dependent variable that can be explained as variations of the independent variable. To create a statistically representative model of solar power generation, the concept of gradient descent optimization (GDO) was considered due to its ability to minimize model error by the LSR of a linear regression model, which is commonly used in estimation studies, but is not as complicated to implement as an intelligent technique [29,41,42].

The goal of GDO is to find an estimate of the actual output through a comparison with all the data collected, as shown in equation (4).

Results and Discussion 1. Hermosillo Site

Figures 17 and 18 show the scatter plot of the estimate versus actual electrical power for Hermosillo and Mexico City, respectively. The second was the percentage of the MAE known as Mean Absolute Percentage Error (MAPE), defined by equation (12). With respect to the data of HS and MCS, Table 9 shows the values ​​of the errors mentioned in equations (11) and (13).

A study of the relationship between weather variables and electrical power demand within a smart grid/smart world framework.Sensors.

Figure 3. The geographical location of the Hermosillo Site (HS) [49] (29.0843293 N, − 110.9583558 W).
Figure 3. The geographical location of the Hermosillo Site (HS) [49] (29.0843293 N, − 110.9583558 W).

Optimal Strategy to Select Load Identification

Features by Using a Particle Resampling Algorithm

Experiments and Results

Table 3 shows the timing of the change point obtained by the original CUSUM load event detection [23]. Referring to Table 1, it can be found that the value of the achieved active power and reactive power was within the range of air conditioning, rather than the range of other devices. Here, we used it to demonstrate the performance of event detection and feature extraction.

This part presents the performance of the proposed bilateral CUSUM event detection method and makes a comparison with the original CUSUM method over REDD.

Figure 5. Active and reactive power diagrams of different devices: (a) induction cooker, (b) kettle, (c) rice cooker, (d) air conditioner, and (e) microwave oven.
Figure 5. Active and reactive power diagrams of different devices: (a) induction cooker, (b) kettle, (c) rice cooker, (d) air conditioner, and (e) microwave oven.

Conclusions and Future Works

An error-correcting framework for sequences derived from known models of state transitions in unobtrusive load control.Adv. A systematic approach to device classification using k-nearest neighbors and naïve Bayesian classifiers for energy efficiency. Energy efficiency. A systematic approach to ON-OFF event detection and clustering analysis of unobtrusive device load control. Front.

Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load detection algorithms.Energy Build.

Analysis of Heat Transfer and Thermal Environment in a Rural Residential Building for Addressing

Energy Poverty

Results

It can be seen from Figure 9 that the heat flux in the middle part of the partition between the master bedroom and the second room was 3.04 W/m2. Figure 10c also shows a cross-section of the speed distribution in the northerly wind simulation. Wind speeds at the top of the roof are much higher in a westerly wind than in a northerly wind.

Wind speeds at 0.5 m to the walls of the building: (a) south wall in the north wind, (b) north wall in the north wind, (c) south wall in the west wind, and (d) north wall in the west wind.

Discussion

The surface temperature of the west side wall of the master bedrooms was lower than the room temperature between 10:00 A.M. From the heat flow rate calculations, the net heat flow rate from the second bedroom is 205.2 W. The average surface temperature of the west side wall of the master bedroom is 9.45 °C, which is very close to the average air temperature of 9.48 °C in the master bedroom.

When a northerly wind speed of 10 m/s was simulated, wind speeds 0.5 m from the building in the southern part of the building were found to vary from 1.1 to 4 m/s.

Effects of Configurations of Internal Walls on the Threshold Value of Operation Hours for Intermittent

Model Setup and Validation 1. Computational Domain

To evaluate the heat transfer to the adjacent room, the variation of the heat flow between the outer surface (close to the adjacent room) of the inner wall and the adjacent air is shown in Figure 9. This is due to the temperature difference of the outer surface between the four inner walls. Tin,i Temperature of the inner surface of the inner wall (◦C) Tin,o Temperature of the outer surface of the inner wall (◦C).

Factors affecting the accuracy of measuring in situ wall heat transfer coefficient using the heat flow meter method. Energy Construction.

Risk-Constrained Optimal Chiller Loading Strategy Using Information Gap Decision Theory

Proposed IGDT-Based OCL Strategy

Hình ảnh

Figure 3. Hs scatter diagrams: a) SWAN without DA and b) SWAN with DA (right), results corresponding to the 15-year time interval 1999–2013.
Figure 7. The hierarchical structures of the fuzzy analysis network process (FANP).
Figure 9. Final performance in technique for order of preference by similarity to ideal solution (TOPSIS) model.
Figure 6. SLS Information platform displaying home page for signing in. http://europruning.mobitron.
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