Appadurai et al 2024a - Revision history

Rimni at 12:21, 3 May 2024

2024-05-03T12:21:10Z

Rimni at 12:18, 3 May 2024

2024-05-03T12:18:59Z

Rimni at 12:18, 3 May 2024

2024-05-03T12:18:21Z

Rimni at 12:14, 3 May 2024

2024-05-03T12:14:41Z

Rimni at 12:14, 3 May 2024

2024-05-03T12:14:11Z

Rimni at 12:12, 3 May 2024

2024-05-03T12:12:14Z

Rimni: /* References */

2024-05-03T12:10:45Z

‎References

Rimni: /* 5. Conclusion and Future Work */

2024-05-03T12:10:20Z

‎5. Conclusion and Future Work

Show changes

Rimni at 10:00, 3 May 2024

2024-05-03T10:00:34Z

Rimni: /* 3.4.8. Hyper-parameter tuning using Artificial Butterfly Algorithm */

2024-05-03T09:56:06Z

‎3.4.8. Hyper-parameter tuning using Artificial Butterfly Algorithm

@@ Line 78: / Line 78: @@
 The methodology for estimating charge patterns is described below. We define the issue, outline the dataset, emphasize data pretreatment, and go over training strategies for several learning models.
-===3.1. EV charging behaviour===
+===3.1 EV charging behaviour===
 Assuming <math display="inline">{t}_{con}</math> depicts the charging behaviour during a session, where <math display="inline">t_{join}</math> represents the period, the car initially plugs in, <math display="inline">t_{discon}</math> reflects the period the car unplugs, and <math>e</math> indicates the total amount of energy supplied to the car. <math display="inline">{B}_{session}</math> as subsequent:
@@ Line 105: / Line 105: @@
 |}
-===3.2. Dataset description===
+===3.2 Dataset description===
 In order to forecast charging actions, we use data on events in addition to the charging information. We will provide a high-level overview of the datasets we employed, focusing on their salient features.
@@ Line 117: / Line 117: @@
 Considering the location on the Caltech campus, we thought it would be interesting to examine whether or not the frequency with which campus activities occur influences charging patterns. Caltech's online calendar [32] was consulted to determine how many events occur per hour. To keep things simple, we rounded times to the nearest hour, so that something that began at 10:20 a.m. would be recorded as having begun at 10 a.m.
-===3.3. Data preprocessing===
+===3.3 Data preprocessing===
 To guarantee accurate predictions, it is essential to clean and preprocess the dataset. Among these is the elimination of anomalies and inaccurate data. Model performance may suffer if outliers are present. Boxplots are frequently used for this purpose. [[#img-1|Figure 1]] shows that there were outliers in the boxplots for both dependent variables. We see that the distribution of outlying values is not the same for both variables; in this case, there are much more extreme values for energy use than for session length. Some cars may have much higher energy consumption, even if the sitting time is not very long.
@@ Line 136: / Line 136: @@
 The pandas [35] package was used to transform the time-series fields into date-time objects, which allowed for easier data merging. We then saved the closest time belonged in order to acquire weather, traffic, and events for a convinced charge record. For instance, a connection time of 22:11 indicates that it is currently 10 p.m. Because of this, we can simply parse out the remaining data. We didn't just pick the volume of traffic at a particular hour; we picked the volume of traffic from the time we got there till the time we left. For example, if a car checked in at 2:00 p.m., we included that car's arrival as part of the day's total traffic count. This would teach the model how changes in traffic volume affect pricing. Similarly, we accounted for everything that happened from the time we landed to the close of the day.
-===3.4. Classification using deep learning networks===
+===3.4 Classification using deep learning networks===
 With the advent of deep residual networks, deep learning's capacity for characterization and learning has been substantially enhanced, making this area of study increasingly popular among categorization researchers. When performing feature extraction using a standard deep convolutional neural network, the deep residual network may take use of the residual construction to maximize the info loss induced by the convolution procedure. This significantly enhances the reliability of rice disease detection. In 2017, a deep residual model called DenseNet was suggested at CVPR as a new type of network model. To promote feature reuse, the model employs tightly coupled connectivity, wherein all levels have access to the feature maps of their previous layers. This directly contributes to the model becoming more condensed and robust against overfitting. It also enables implicit deep supervision by having the loss function directly supervise each layer through a shortcut path..
-====3.4.1. Dense connection====
+====3.4.1 Dense connection====
 DenseNet is an alternative connection design to traditional networks that maximizes information transfer during picture recognition by employing dense connectivity. This indicates that in DenseNet, all of the layers above it are linked together. The goal is to maximize the transfer of knowledge between networks by having each successive layer use the feature maps it has obtained layer as input to the next layer.
@@ Line 230: / Line 230: @@
 |}
-===3.4.2. Transition===
+===3.4.2 Transition===
 In DenseNet, downsampling operations are handled by the transition module. For the model to be beaten to lower the sum of channels dense module, the changeover module primarily conducts two operations: pooling, with an layer of (2 2).
-===3.4.3. Attentional mechanisms===
+===3.4.3 Attentional mechanisms===
 When processing an image, network to focus on the parts of the duplicate that will have the most impact on the final result. The convolutional neural network may dynamically modify its focus thanks to the attention mechanism. natural language processing have both benefited from the application of attention methods. Recurrent neural networks with have found widespread use in sequential models. There are two main categories of attention mechanisms: channel attention and spatial attention. Both CBAM models were employed in this work, where the structure is mentioned in [[#img-3|Figure 3]].
@@ Line 267: / Line 267: @@
-====3.4.4. The channel attention mechanism unit====
+====3.4.4 The channel attention mechanism unit====
 The channel care apparatus first receipts the map <math display="inline">F\in {R}^{C\times H\times W}</math> and permits it through global maximum regular pooling to get two <math display="inline">(C\times 1\times 1)</math> maps <math display="inline">{MS\in R}^{C\times 1\times 1}</math>. A two-layer multilayer with <math display="inline">C/r</math> in the first layer <math display="inline">C</math> neurons in the second layer (where <math display="inline">C</math> is the original number of neurons) is then applied to the generated feature maps in a shared network. After then, the instrument <math display="inline">M_C (F)</math> adds up the individual features from the shared network's output to produce the final, full feature vector. Equation (4) depicts the formula for the channel attention apparatus
@@ Line 283: / Line 283: @@
 where <math display="inline">\sigma</math>  function; <math display="inline">{W}_{0}\in {R}^{C/r\times C},\, {W}_{1}\in \, {R}^{C/r\times C}</math>, and the <math display="inline">MLP</math> weights <math display="inline">W_0</math> and <math display="inline">{W}_{1}</math> inputs; and <math display="inline">{W}_{0}</math> is after the ReLU purpose.
-====3.4.5. Spatial attention mechanism module====
+====3.4.5 Spatial attention mechanism module====
 The feature vectors generated by the channel care apparatus module are used as inputs to the spatial attention mechanism. Two feature vectors are generated from the feature vector by first using a maximum pooling process and then an process. <math display="inline">{F}_{max}^{S}\in {R}^{1\times H\times W}</math> and <math display="inline">{F}_{avg}^{S}\in {R}^{1\times H\times W}</math>, respectively. Once the maximum and average pooled characteristics have been summed, a channel splicing operation is performed on them. After that, a convolutional convolution operation (<math display="inline">7\times 7</math>) is performed on the feature vectors to collapse them down to a single dimension. The feature vector is derived after a sigmoid function is applied, <math display="inline">{M}_{S}(F)\in {R}^{H\times W}</math>
@@ Line 299: / Line 299: @@
 where <math display="inline">\sigma</math>  symbolizes the sigmoid purpose and <math display="inline">{f}^{7\times 7}</math> signifies the process with a difficulty kernel of size <math display="inline">(7\times 7)</math>.
-====3.4.6. Res-attention====
+====3.4.6 Res-attention====
 In this research, we built upon the CBAM module to create a novel attention mechanism we name the Res-Attention module. The module was developed to improve gearbox reliability by including a residual structure. When the feature map is sent into the model in the Attention component, some of the information is remembered. When the model is linked after the model, the info produced from the module is combined with the info that was previously preserved. The module then sends out the combined feature data. [[#img-5|Figure 5]] depicts the Res-Attention module's internal construction.
@@ Line 312: / Line 312: @@
-====3.4.7. DRA-net====
+====3.4.7 DRA-net====
 In this research, we examined and built the DRA-Net network perfect by integrating the unit into the perfect to better predict charging behaviour. The DRA-Net architecture is made up of a set of interconnected and layered modules called Dense Blocks, Res-Attention Modules, and Transition Layers. The study attached the module to the Transition module after positioning module. A transition module linked the first three modules together, whereas the fourth ResAttention module was linked straight to the classification layer. Lastly, we adjusted the Classification Layer's output characteristics.
@@ Line 682: / Line 682: @@
 |  style="text-align: center;vertical-align: top;"|2.90
 |}
 === 4.1 Discussion on neural networks for EV charging prediction===

@@ Line 683: / Line 683: @@
 |}
-=== 4.1. Discussion on neural networks for EV charging prediction===
+=== 4.1 Discussion on neural networks for EV charging prediction===
 The prediction of electric vehicle (EV) charging behavior is crucial for optimizing charging infrastructure and balancing energy demands on the grid. Various deep learning models, including AlexNet, ResNet-101, DenseNet-121, VGG-19, MobileNet, and the proposed BOA-DRA-Net, have been employed to tackle this problem. Each of these models brings distinct features and strengths to the challenge.

@@ Line 327: / Line 327: @@
-====3.4.8. Hyper-parameter tuning using Artificial Butterfly Algorithm====
+====3.4.8 Hyper-parameter tuning using Artificial Butterfly Algorithm====
-:1) Exploring the effects of life history optimization on lifetime speculation in violent conduct [36] may be done with countless success using system. You can get some great ideas for a new optimization technique from spotted trees. We introduced the Artificial Butterfly Optimisation (ABO) technique, which is inspired by the mating behaviour of speckled woodlands. [[#tab-2|Table 2]] displays the ABO algorithm's pseudo code. The ABO procedure follows a set of criteria that idealize the way butterflies locate a partner.
+Exploring the effects of life history optimization on lifetime speculation in violent conduct [36] may be done with countless success using system. You can get some great ideas for a new optimization technique from spotted trees. We introduced the Artificial Butterfly Optimisation (ABO) technique, which is inspired by the mating behaviour of speckled woodlands. [[#tab-2|Table 2]] displays the ABO algorithm's pseudo code. The ABO procedure follows a set of criteria that idealize the way butterflies locate a partner:
 :1. If a male butterfly wants to increase his chances of mating with a female, he will fly to a better location known as a sunspot.

@@ Line 22: / Line 22: @@
 ==Abstract ==
-In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry. From the experimental analysis, it is clearly proves that the proposed model achieved nearly 98% to 99% of accuracy, precision, recall and F-score, when compared with existing techniques.
+In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (<math display="inline"> 3\times 3 </math>). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry. From the experimental analysis, it is clearly proves that the proposed model achieved nearly 98% to 99% of accuracy, precision, recall and F-score, when compared with existing techniques.
 '''Keywords''': Electric vehicle, deep residual attention network, artificial butterfly optimization algorithm, charging behaviour information, energy needs

@@ Line 24: / Line 24: @@
 In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry. From the experimental analysis, it is clearly proves that the proposed model achieved nearly 98% to 99% of accuracy, precision, recall and F-score, when compared with existing techniques.
-'''Keywords''': Electric vehicle, deep residual attention network, artificial butterfly optimization algorithm, charging behaviour information, energy eeeds
+'''Keywords''': Electric vehicle, deep residual attention network, artificial butterfly optimization algorithm, charging behaviour information, energy needs
 ==1. Introduction==

@@ Line 713: / Line 713: @@
 [4] Xiong Y., An B., Kraus S. Electric vehicle charging strategy study and the application on charging station placement. Autonomous Agents and Multi-Agent Systems, 35:1-19, 2021.
-[5] Baghali S., Hasan S., Guo Z. Analyzing the travel and charging behavior of electric vehicles-a data-driven approach. In 2021 IEEE Kansas Power and Energy Conference (KPEC), pp. 1-5, IEEE, 2021.
+[5] Baghali S., Hasan S., Guo Z. Analyzing the travel and charging behavior of electric vehicles-a data-driven approach. In 2021 IEEE Kansas Power and Energy Conference (KPEC), IEEE, pp. 1-5, 2021.
 [6] Calearo L., Marinelli M., Ziras C. A review of data sources for electric vehicle integration studies. Renewable and Sustainable Energy Reviews, 151, 111518, 2021.

← Older revision		Revision as of 12:10, 3 May 2024
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	==References==		==References==
		+
		+	<div class="auto" style="text-align: left;width: auto; margin-left: auto; margin-right: auto;font-size: 85%;">

	[1] Kavianipour M., Fakhrmoosavi F., Singh H., Ghamami M., Zockaie A., Ouyang Y., Jackson R. Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior. Transportation Research Part D: Transport and Environment, 93, 102769, 2021.		[1] Kavianipour M., Fakhrmoosavi F., Singh H., Ghamami M., Zockaie A., Ouyang Y., Jackson R. Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior. Transportation Research Part D: Transport and Environment, 93, 102769, 2021.

@@ Line 574: / Line 574: @@
 <div id='tab-5'></div>
-{| style="width: 100%;margin: 1em auto 0.1em auto;border-collapse: collapse;"
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
-|-
+|-style="text-align:center"
-|  style="text-align: center;vertical-align: top;"|'''Optimization Algorithm'''
+!'''Optimization Algorithm''' !! '''lr = 0.1''' !! '''lr = 0.05''' !! '''lr = 0.01''' !! '''lr = 0.005''' !! '''lr = 0.001'''
-|  style="text-align: center;"|'''lr = 0.1'''
-|  style="text-align: center;"|'''lr = 0.05'''
-|  style="text-align: center;"|'''lr = 0.01'''
-|  style="text-align: center;"|'''lr = 0.005'''
-|  style="text-align: center;"|'''lr = 0.001'''
 |-
 |  style="text-align: center;vertical-align: top;"|GO
@@ Line 597: / Line 592: @@
 |  style="text-align: center;vertical-align: top;"|90.56
 |-
-|  style="text-align: center;vertical-align: top;"|<span id='_Hlk145823576'></span>BOA
+|  style="text-align: center;vertical-align: top;"|BOA
 |  style="text-align: center;vertical-align: top;"|98.11
 |  style="text-align: center;vertical-align: top;"|98.90
@@ Line 616: / Line 611: @@
 {| class="wikitable" style="margin: 0em auto 0.1em auto;border-collapse: collapse;width:auto;"
 |-style="background:white;"
-|style="text-align: center;padding:10px;"| [[Image:Draft_Thirumalraj_439606864-image10.png|600px]]
+|style="text-align: center;padding:10px;"| [[Image:Draft_Thirumalraj_439606864-image10.png|500px]]
 |-
 | style="background:#efefef;text-align:left;padding:10px;font-size: 85%;"| '''Figure 9'''. Accuracy Analysis for various optimization models
@@ Line 648: / Line 643: @@
 {| class="wikitable" style="margin: 0em auto 0.1em auto;border-collapse: collapse;width:auto;"
 |-style="background:white;"
-|style="text-align: center;padding:10px;"| [[Image:Draft_Thirumalraj_439606864-image11.png|600px]]
+|style="text-align: center;padding:10px;"| [[Image:Draft_Thirumalraj_439606864-image11.png|500px]]
 |-
 | style="background:#efefef;text-align:left;padding:10px;font-size: 85%;"| '''Figure 10'''. Graphical Representation for kernel size variations

@@ Line 339: / Line 339: @@
 <div class="center" style="font-size: 85%;">'''Table 2'''. Pseudo code of ABO algorithm</div>
-<div id='tab-1'></div>
+<div id='tab-2'></div>
 {| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
 |-style="text-align:left"