Machine Learning Tools in the Predictive Analysis of ERCOT Load Demand Data

The electric load industry has seen a significant transformation over the last few decades, culminating in the establishment and implementation of electricity markets. This transition separates electric generation services into a distinct, more competitive sector of the industry, allowing for the introduction of greater unpredictability into the system. Forecasting power system load has developed into a core research area in power and energy demand engineering in order to maintain a constant balance between electricity supply and demand. The purpose of this thesis dissertation is to reduce power system uncertainty by improving forecasting accuracy through the use of sophisticated machine learning techniques. Additionally, this research provides sophisticated machine learning-based forecasting methodologies for the three forecasting professions from a variety of perspectives, incorporating several advanced deep learning features such as Naïve/default, Hyperparameter Tuning, and Custom Early Stopping. We begin by creating long-term memory (LSTM) and gated recurrent unit (GRU) models for ERCOT demand data, and then compare them to some of the most well-known supervised machine learning models, such as ARIMA and SARIMA, to identify the best set of models for long- and short-term load forecasting. We will also use multiple comparison approaches, such as the radar chart and the Pygal radar chart, to perform a thorough evaluation of each of the deep learning models before settling on the best model

Machine Learning Model Optimization with Hyper Parameter Tuning Approach

Hyper-parameters tuning is a key step to find the optimal machine learning parameters. Determining the best hyper-parameters takes a good deal of time, especially when the objective functions are costly to determine, or a large number of parameters are required to be tuned. In contrast to the conventional machine learning algorithms, Neural Network requires tuning hyperparameters more because it has to process a lot of parameters together, and depending on the fine tuning, the accuracy of the model can be varied in between 25%-90%. A few of the most effective techniques for tuning hyper-parameters in the Deep learning methods are: Grid search, Random forest, Bayesian optimization, etc. Every method has some advantages and disadvantages over others. For example: Grid search has proven to be an effective technique to tune hyper-parameters, along with drawbacks like trying too many combinations, and performing poorly when it is required to tune many parameters at a time. In our work, we will determine, show and analyze the efficiencies of a real-world synthetic polymer dataset for different parameters and tuning methods

Single Axis Solar Tracker for Maximizing Power Production and Sunlight Overlapping Removal on the Sensors of Tracker

This paper presents the design and execution of a solar tracker system devoted to photovoltaic (PV) conversion panels. The proposed single-axis solar tracker is shifted automatically based on the sunlight detector or tracking sensor. This system also removes incident sunlight overlapping from sensors that are inside the sunlight tracking system. The Light Dependent Resistor (LDR) is used as a sensor to sense the intensity of light accurately. The sensors are placed at a certain distance from each other in the tracker system to avoid sunlight overlapping for maximum power production. The total system is designed by using a microcontroller (PIC16F877A) as a brain to control the whole system. The solar panel converts sunlight into electricity. The PV panel is fixed with a vertical axis of the tracker. This microcontroller will compare the data and rotate a solar panel via a stepper motor in the right direction to collect maximum photon energy from sunlight. From the experimental results, it can be determined that the automatic (PV solar tracker) sun tracking system is 72.45% more efficient than fixed panels, where the output power of the fixed panel and automatically adjusted panel are 8.289 watts and 14.287 watts, respectively

Performance evaluation of WS2 as buffer and Sb2S3 as hole transport layer in CZTS solar cell by numerical simulation

Abstract This study reports on performance enhancement of a Cu2ZnSnS4 solar cell introducing Sb2S3 as hole transport layer (HTL) along WS2 as buffer layer. We have investigated photovoltaic (PV) characteristics by utilizing SCAPS‐1D. A comparative analysis on PV performances between conventional CZTS/CdS and proposed Ni/Sb2S3/CZTS/WS2/FTO/Al solar cells is presented. It is revealed that “spike like” band structure at the CZTS/WS2 interface having smaller conduction band offset makes it potential alternative to commonly used CdS buffer. This report also evaluates that the Sb2S3 as an HTL inserted at the rear of CZTS enhances performances by reducing carrier recombination at back interface with appropriate band alignment. The impacts of thickness, carrier concentration of different layers, and bulk defect density in CZTS as well as the interface defects on cell outputs are analyzed. The influences of temperature, work function, and cell resistances are also examined. Optimum absorber thickness of 1.0 μm along doping density of 1017 cm−3 is selected. A maximum efficiency of 30.63% is achieved for the optimized CZTS cell. Therefore, these results suggest that Sb2S3 as HTL and WS2 as buffer layer can be employed effectively to develop highly efficient and low‐cost CZTS solar cells

Proceedings of International Conference on Emerging Trends in Engineering and Advanced Science

This conference proceedings contains articles on the various research ideas of the academic community and practitioners presented at the International Conference on Emerging Trends in Engineering and Advanced Science (ICETEAS-2021). ICETEAS-2021 was organized by the Department of Electrical and Electronic Engineering & Department of Mechatronics Engineering, World University of Bangladesh, Dhaka, Bangladesh on July 4th-5th November 2021. Conference Title: International Conference on Emerging Trends in Engineering and Advanced ScienceConference Acronym: ICETEAS-2021Conference Date: 4-5 November 2021Conference Location: Online (Virtual Mode)Conference Organizers: World University of Bangladesh, Dhaka, Bangladesh