11,407 Works

25 kVA Wind -Part 1

Abhishek Bansal
This dataset is in support of my 2 original Research Papers - (i) " Grid-Connected Wind " and (ii) submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60).This dataset is related toThis experiment is different from the exisiting approaches in the following ways(a) Simulation takes into account non-linearity viz, damper is non- linear, torque is not constant, backlash from the gears(b)(c)(d)(e)This simulation is designed taken into consideration the standards for the India, Delhi conditions, where this...

32 kVA wind - Part 1

Abhishek Bansal
This dataset is in support of my original Research Paper " Grid-Connected Wind " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)This experiment is similar in but as in those datasets, it was concluded that .,so, similar not repeated this dataset do notco butusaAbstract of the above submitted paper

submission_11142020

Ameema Zainab
I have used decision trees to perform my analysis

Team14_5C_2020_SoCSE_KLETech

Energy Consumption

grupo2_test2

Another try.

MATLAB code to test SPIM approach for SS

Jorge Y. Hernández García
MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different...

6.66 kVA Solar

Abhishek Bansal
This dataset is in support of my original Research Paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)Simulation is made to support the earlier proposed project (rejected) for funding.This is not connected to the grid but the other related dataset " " , where this will b conencted to the inverter and will be connected to the grid.This experiment is different from the exisiting approaches in the following ways(a) In the...

6.66 kVA Solar Inverter

Abhishek Bansal
This dataset is in support of my original Research Paper " 6.66 kVA Solar Inverter " submitted to "IEEE Transactions on Power Electronics" (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=63)This experiment is different from the exisiting approaches in the following ways(a)(b) Non-Linear(c)(d)(e)Abstract of the above submitted paper

Model 2.1.1

Konstantinos Theodorakos
Model 2.1.1 Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering with nearest neighbors.2. Clustering using (daily kWh) consumption extracted features. Cluster count set to 2-4: final count is decided by maximum silhouette score of manhattan distances.3. Meters not in the training clustering data, are classified to a specific cluster using a Gaussian Process Classifier (GPC) on the same dataset (12 classifiers total). Features (170)...

Coronavirus (COVID-19) Tweets Sentiment Trend (Global)

Rabindra Lamsal
This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development...

6.66 kVA Solar

Abhishek Bansal
This dataset is in support of my original research paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.This is not connected to the grid. The other related dataset " " , where this will be connected to the inverter which in turn will be connected to grid.This experiment is different from the exisiting approaches in the ways asAbstract of the above submitted paper

6.66 kVA Solar

Abhishek Bansal
This dataset is in support of my original research paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.This is not connected to the grid. The other related dataset " " , where this will be connected to the inverter which in turn will be connected to grid.Though few similar kind papers are there, this paper is original and different from them as eviddent from the abstract and...

6.66 kVA Solar

Abhishek Bansal
This dataset is in support of my original research paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.This is not connected to the grid. The other related dataset " " , where this will be connected to the inverter which in turn will be connected to grid.Though few similar kind papers are there, this paper is original and different from them as eviddent from the abstract and...

Model 2.1.4

Konstantinos Theodorakos
Model 2.1.4 Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering with nearest neighbors.2. Clustering using (daily kWh) consumption extracted features. Cluster count set to 2-4: final count is decided by maximum silhouette score of manhattan distances.3. Meters not in the training clustering data, are classified to a specific cluster using a Gaussian Process Classifier (GPC) on the same dataset (12 classifiers total). Features (170)...

Team14_5C_2020_SoCSE_KLETech(1)

Energy consumption is predicted for 3248 households using smart meter data.

Monthly Energy Prediction from Smart Meter Data

Datasets provided:

AI Boffins

Mrityunjoy Panday
Timeseries is a sequence taken at successive equally spaced points in time. Rise and fall of stock prices, weather etc. are all different examples of timeseries. Due to its property of persistence, timeseries observations change slowly. Also, past observations provide information about current observations.EON[1] wants to predict a customer’s annual consumption with lesser amount of data as a fairly new technology of smart meters has less historical data. This will help them configure direct debit...

Team15_5B_2020_SoCSE_KLETech

Datasets provided: 1. Consumption.csv:Half hourly consumption in "kwh" of "" meters for a year is given.Null values %: 51.6%2. Weather-avg/min/max.csv:Per day average/minimum/maximum temperature of meter for a year is given.3. AddInfo.csv:Some additional information for some households is provided but more than 95% values are null. The attributes "dwelling_type" and "no. of bedrooms" holds good amount of data with 24% and 13% null values respectively. Data Preprocessing:Firstly, We assigned a number to each meter id and...

25 kVA Wind -Part 1

Abhishek Bansal
This dataset is in support of my original research paper " Grid-Connected Wind " submitted to "IEEE Transactions on Power Electronics" (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=63). Abstract of the above submitted paper

EmoSurv: A typing biometric (Keystroke dynamics) dataset with emotion labels created using computer keyboards

Aicha Maalej & Ilhem Kallel
EmoSurv is a dataset containing keystroke data along with emotion labels. Timing and frequency data is recorded while participants are typing free and fixed texts before and after being induced specific emotions. These emotions are: Anger, Happiness, Calmness, Sadness, and Neutral state.First, data is collected while the participant is in a neutral state. Then, the participant watches an eliciting video. Once the emotion is induced in the participant, he types another fixed and free text.EmoSurv...

EmoSurv: A typing biometric (Keystroke dynamics) dataset with emotion labels created using computer keyboards

Aicha Maalej & Ilhem Kallel
EmoSurv is a dataset containing keystroke data along with emotion labels. Timing and frequency data is recorded while participants are typing free and fixed texts before and after being induced specific emotions. These emotions are: Anger, Happiness, Calmness, Sadness, and Neutral state.First, data is collected while the participant is in a neutral state. Then, the participant watches an eliciting video. Once the emotion is induced in the participant, he types another fixed and free text.EmoSurv...

Baseline 8e

Same as method as described in baseline 8c, exept that the monthly data have been corrected using a factor. This factor has been computed according to the number of missing days in each month.

6.66 kVA Solar

Abhishek Bansal
This dataset is in support of my original research paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.This is not connected to the grid. The other related dataset " " , where this will be connected to the inverter which in turn will be connected to grid.Though few similar kind papers are there, this paper is original and different from them as evident from the abstract and...

Baseline 8f

Same as baseline 8c, with a correcting factor on the monthly aggregated data, taking in ccount the (number of missing days-2), considering that there will also be missing days in the predicted data.

AI_QSample_002

SG Quek Selvachandran & Ganeshsree
During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase...

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