We benchmark state-of-the-art methods for forecasting electricity demand on the household level. Our evaluation is based on two data sets containing the power usage on the individual appliance level. Our results indicate that without further refinement the considered advanced state-of-theart forecasting methods rarely beat corresponding persistence forecasts. Therefore, we also provide an exploration of promising directions for future research.
Hate Speech is a widespread problem that degrades a person or people based on their race, religion, gender or disability. This research work proposes a tool to raise awareness on the persistent hate speech in social media platforms. The primary aim of this research work is to highlight the content that promotes violence or hatred against individuals or groups based on religion, gender or ethnicity. Logistic regression is a technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems. Using this algorithm, the model trains itself from the dataset and identifies and displays the sentiment of the tweets. Also, to get the real-time analysis on tweets, Twitter API and libraries such as Tweepy and Textblob are used. The proposed model has the ability to detect the appropriate sentiment with 83.98 percent accuracy. The tool is made free and available for demo use to thepublic
The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage. The ability to forecast consumption at different locations in distribution systems will be a key capability of Smart Grids. The goal of this paper is to benchmark state-of-the-art methods for forecasting electricity demand on the household level across different granularities and time scales in an explorative way, thereby revealing potential shortcomings and find promising directions for future research in this area. We apply a number of forecasting methods including ARIMA, neural networks, and exponential smoothening using several strategies for training data selection, in particular day type and sliding window based strategies. We consider forecasting horizons ranging between 15 minutes and 24 hours. Our evaluation is based on two data sets containing the power usage of individual appliances at second time granularity collected over the course of several months. The results indicate that forecasting accuracy varies significantly depending on the choice of forecasting methods/strategy and the parameter configuration. Measured by the Mean Absolute Percentage Error (MAPE), the considered state-of-the-art forecasting methods rarely beat corresponding persistence forecasts. Overall, we observed MAPEs in the range between 5 and >100%. The average MAPE for the first data set was 30%, while it was 85% for the other data set. These results show big room for improvement. Based on the identified trends and experiences from our experiments, we contribute a detailed discussion of promising future research.
The purpose of this report is to identify /observe and determine the pattern of velocity profile and pressure distribution by using CFD simulation program after the 3D design and modeling of the pump is made using Vista CPD. We have also created a Solid model using Fusion 360 to get a clear idea of Centrifugal pump design. Basically, this report revolves around the idea of investigating the effect and distribution of velocity profile and pressure within a pump having the following specification, Head = 20 m, Flow rate = 100 m3 /hr, and RPM = 2000. 3D Navier–Stokes equations were solved using ANSYS CFX. The standard k −εturbulence model was chosen for the turbulence model. From the design point of view, we have studied the effects of different parameters like rotational speed, volume flow rate etc on the impeller and volute. From the simulation results it was observed that the pressure increases gradually from impeller inlet to outlet. The static pressure on the pressure side is evidently larger than that on the suction side at the same impeller radius. In addition to this, it was observed that the velocity increases from the impeller inlet until it enters the volute casing. It then drops to a minimum value at the outlet region.
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