Residential Load Forecasting based on Deep Neural Network

This paper presents residential load forecasting using multivariate multi-step Deep Neural Networks (DNN) such as LSTM, CNN, Stacked LSTM, and Hybrid CNN-LSTM. A preliminary Exploratory Data Analysis (EDA) is conducted, and the decision variables are identified. An elbowing method is used to determine the number of clusters. Data is categorized based on weekdays, weekends, vacations, and Covid-Lockdown. Dimensionality-reduction using principal component analysis (PCA) is conducted. Seasonality-based clustering is found to improve the DNN model prediction accuracy further. A comparative analysis employs error metrics such as RMSE, MSE, MAPE, and MAE. The multivariate LSTM model with feedback is found to be the best fit model with the better performance indices. © 2023 IEEE.

Energy Audit in an Urban Indian Residential Community-A Case Study

This paper presents an energy audit study conducted for an urban residential community in Mumbai. The consumers are categorized using a k-means clustering algorithm based on their electricity consumption. The energy-efficient appliance selection is undertaken by a benchmarking study based on the appliance energy labeling and star rating initiated by the Bureau of Energy Efficiency(BEE) in India. The study establishes the techno-economic feasibility of energy savings in Indian urban households with an average payback period of 3.3 years. The energy-saving opportunities are selected based on each cluster's capital cost and payback period. Sensitivity analysis of electricity tariff of a region on payback period is undertaken. The covid impact analysis on the residential energy consumption is conducted by comparing energy consumption before and after the covid. The benefits are replicable in most Indian households, especially the urban residential consumers with high consumption in regions with high electricity tariffs. © 2022 IEEE.

Virtual Learning Assistance for Students

Virtual learning platforms are important for the future of education, especially during unprecedented times like the current covid-19 pandemic. Such learning platforms are expected to be interactive and help students communicate better with teachers and other students even virtually. This research work intends to develop a virtual learning platform in the form of a website that allows teachers to connect with students via individual and group video conferencing, create basic quizzes for the students, easily evaluate the quizzes and monitor student attendance. This website would also be useful for the students as it allows them to learn better by understanding answers for the graded quizzes. It also allows the students to view their obtained marks, check their attendance, have one-to-one video interaction with the teachers using a WEBRTC technique and Python Django framework, and, much more, all in a single platform. All the data are stored and manipulated in the MYSQL database. Thereby serving as a one-stop approach for every need of a student without having multiple websites and thereby creating a hassle out of it. The entire front end was developed entirely using web technologies like HTML, CSS, Javascript. © 2021 IEEE.