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With the increasing demand for energy worldwide, it has become imperative to explore and adopt sustainable energy practices. In this regard, predicting household electricity consumption accurately is essential, as it can help optimize energy usage and reduce waste. This paper focuses on using Long Short-Term Memory (LSTM) shows the neural networks to prevent household electricity consumption in India. The LSTM model was implemented using the Keras library in Python. The model was trained using the training set and tested using the testing set. The study demonstrated that LSTM neural networks are a promising approach to predicting household electricity consumption in India. Further research can be conducted to recover the accuracy of the perfect by including more variables such as household size, income, and appliance usage. [ABSTRACT FROM AUTHOR]

India is one of the largest countries of the world with a high population density. Being a developing country, this nation is facing enormous challenges in regulating its air quality. Population growth, vehicular ownership, urbanization, demand of energy and industrialization process are driving the pollution level to an alarming state in Delhi which is the capital of India. Accurate air quality index (AQI) forecasting of Delhi will assist Government in making strategic decisions so as to avoid catastrophic deterioration in the quality of healthy air. Motivated from this, in this paper, for the first time a systematic study is made to assess the effectiveness of four promising univariate statistical forecasting models and twelve machine learning (ML) models in predicting the AQI of Delhi. Four accuracy measures including "mean absolute scaled error (MASE), symmetric mean absolute percentage error (SMAPE), root means square error (RMSE) and mean absolute error (MAE)" are used to assess the performance of statistical and ML models. Extensive statistical analyses on obtained results using Wilcoxon Signed-Rank test illustrate the superiority of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models than other considered models in predicting the Delhi AQI. [ABSTRACT FROM AUTHOR]

Over the years, the gold prices have been increasing rapidly. Covid-19 and its impact leads to rise in the prices of Gold in the year 2020. So many variables are mindful for increasing the gold cost in India and it leads to investment decisions of individuals and enterprises. Autoregressive Integrated Moving Average (ARIMA) is useful method to gauge time series data. In the paper we mainly focus on daily costs of Gold from the year 2018 to 2020 to determine and forecast the daily gold prices in 2021. Also estimate the error (%) between the observed and estimated values through ARIMA model. This study will provide the estimates of suitable ARIMA model (0,1,2) along with Autocorrelation function (ACF) & Partial autocorrelation function (PACF) from the selected data, The auxiliary source information shows the positive patterns for getting effectiveness, For quantitative examination and speculation selections of financial backers. [ABSTRACT FROM AUTHOR]

In this world needs to consider the elective asset of the ability of the work which is separated from traditional vivacity assets (biogas, coal and so on). The main valuable things in the world are done by solar energy & huge capability of environmentally friendly power. India has a tremendous potential for creating green power from the environmentally friendly power sources. For advance development of power production in from renewable energies, the public sectors in India try in their best to set up a strong alternative resource. Prime minister was announced a scheme for renewable energy which has framed by Jawaharlal Nehru National Solar scheme. This scheme has set a good objective to build up 25,000 MW projects in various sites in India like some unique steps following as (a) lifelong strategy; (b) enormous scope organization objectives; (c) management (d) homegrown creation in crude materials segments, therefore to expert in the framework similarity by 2024. In this study plan is help for improvement the target and. A solar energy preliminary diagram is given by our paper. It surveys the present status of solar energy as far as present limit, alongside authentic patterns of sun powered energy and future capability of an alternate type of renewable energy. [ABSTRACT FROM AUTHOR]