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The main aim of this paper is to diagnose the impact of climate change with the analysis of its scenario, causes and its solutions in relation to energy sector. Initially, this paper discusses the scenario of climate change in India. Afterward, a comparative difference analysis of selected climatic variables is performed on the chosen areas and also determines the relationships among these variables. Both these objectives are performed by ANOVA, post-hoc, correlation and linear regression analysis. Subsequently, the forecasting of several climatic variables is performed from 2021 to 2025 by time series analysis utilizing the ARIMA (Autoregressive Integrated Moving Average) model. Then, this paper investigates the interdependency scenario between the climate change and the electricity sector. Afterward, this paper discusses some major impacts of climate change on the electricity sector. Then, this paper presents the present electricity scenario of India and the renewable power potential of India. Subsequently, this paper suggests some open research challenges with recommendations for improving the energy sector of India. Results of this diagnoses analysis concludes that the climate change and energy sector are mutually connected, and the climate change impacts can be reduced with the improvement in energy sector using renewable energy. • A comparative difference analysis of selected climatic variables is performed on the chosen seven areas and also determines the relationships among these variables. • Forecasting of selected climatic variables is performed from 2021 to 2025 by time series analysis. • Interdependency scenario between the climate change and the electricity sector is analyzed. • Present electricity scenario of India and the renewable power potential of India are illustrated. • Open research challenges with recommendations are suggested for improving the energy sector of India. [ABSTRACT FROM AUTHOR]

Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) models are common techniques that are widely used in analysing and forecasting stationary and seasonal time series data. The three essential steps involved to construct ARIMA are identification, estimation, and checking the validity of the model. The most critical step followed in constructing the ARIMA model is model identification. However, overcoming the difficult local optima problem for both ARIMA and SARIMA is still challenging as there is no appropriate method to solve it. In this paper, the proposed parallel GA-SARIMA model is used to solve the problem of local optima, where the genetic algorithm (GA) is used at the initial stage to identify the order and estimation of the parameters for SARIMA. The National Climate Data Centre (NCDC) time series dataset is used for testing the efficiency of the final parallel GA-SARIMA model to forecast the mean temperature of India from 2000 to 2017. The GA algorithm is successfully implemented to solve the optimization problems by introducing better solutions suitable for SARIMA models. The results of the study showed that the implementation of the combined approach of parallel GA and SARIMA enhances the prediction accuracy of the model. The parallel GA-SARIMA method is particularly robust, faster and performs better than sequential SARIMA models in terms of running time and cost function values. [ABSTRACT FROM AUTHOR]

Environmentally conscious manufacturing (ECM) has become an important strategy and proactive approach for the iron and steel sector of India to produce environment friendly and to reduce manufacturing cost. There are several environmentally conscious manufacturing indicators to evaluate ECM programs. Among those indicators, energy consumption and greenhouse (GHG) emission may be considered critical environmentally conscious manufacturing indicators (CECMI) for Indian iron and steel sector. This paper focuses on forecasting energy consumption and GHG emission for a pig iron manufacturing organization of India because the managers are interested to know the current and future trends of these indicators for better environmental policy. For forecasting purpose, autoregressive integrated moving average (ARIMA) is applied to reveal that ARIMA (1,0,0) × (0,1,1) is the best fitted model for energy consumption. Regarding GHG emission, ARIMA (0,1,4) × (0,1,1) is the best fitted model. In both cases, the forecasts resemble those of the seasonal random trend model, however they appear smoother because the seasonal pattern and the trend are efficiently averaged for energy consumption and as well as GHG emission. Selection of the correct ARIMA models for these indicators will help in accurate forecasting in order to achieve better environmental management practice. [ABSTRACT FROM AUTHOR]

Developing more effective carbon mitigation strategies requires accurate forecasting of carbon emissions. This is especially true for China, the United States and India, which produce over half of the world's carbon emissions. China, the United States, and India represent three different types of carbon emissions data. The first data type is typical for China: rapid grows which is thereafter slowing down. The US carbon emissions data show volatile growth and decline. The third data type is common for India's data: accelerated growth. To deal with the volatility of the data and to improve the forecasting accuracy, this paper combines Metabolic Nonlinear Grey Model (MNGM) with Autoregressive Integrated Moving Average (ARIMA) to develop the combined MNGM-ARIMA model, and MNGM with Back Propagation Neural Network (BPNN) to develop the new combined MNGM-BPNN model. In this way, ARIMA and BPNN could correct the modelling residual error of MNGM, to decrease the forecasting error. Through the quantitative data study of the countries in focus, we have made the following findings: First, the mean relative percent errors of the proposed MNGM-ARIMA and MNGM-BPNN models are 1.35% and 1.57%, which are lower than the traditional MNGM, ARIMA, and BPNN models. Besides, it is projected that during the period of 2019–2030, the US′ carbon emissions will keep a downward trend, while carbon emissions in China and India will continue to grow. Finally yet importantly, in the future periods of time, the carbon emissions growth rate in India will be faster than that for China. The above findings have led us to the following conclusions: (1) Error values of those two proposed approaches have proven that the strategy of correcting the forecasting error of the previous models with the latter models can effectively improve the forecasting accuracy. (2) The proposed two combined forecasting techniques have shown sound performance for the three types of data. It is reasonable to believe that new approaches will help to improve insight into better forecasting carbon emissions in other countries and regions of the world. (3) China and India will remain the major sources of global carbon emissions, and should therefore continue their efforts on carbon emission reduction. Image 10221 • China, the United States and India contribute half of global carbon emissions. • Forecasting the carbon emissions is conducive to global carbon emission reduction. • Strategy of "error correction + quadratic modeling" and new models are proposed. • Mean relative errors of the two proposed models achieved 1.35% and 1.57%. • Future carbon emissions growth will be concentrated in developing countries. [ABSTRACT FROM AUTHOR]

Time-series modeling forms an important area of research in geophysics. Time-series models can be linear, like linear state-space models or non-linear, like artificial neural networks. One way to judge the goodness of different models associated with a given time-series is to assess the prediction capabilities of these models. Some of the important techniques used in time-series forecasting are: (i) Minimum mean squared error (MMSE) forecast obtained using conditional means of ARIMA(p,d,q) models, (ii) Kalman filter approach and (iii) Artificial neural networks (ANN) approach. However, the wavelet-based versions of these techniques, respectively denoted as W-MMSE, W-Kalman and W-ANN, rather than the original techniques themselves, have been found to possess better capabilities in forecasting the highly nonlinear geophysical data. Using the original and predicted data, these observations have been validated by determining the RMSE (root mean squared error) and correlation coefficients between them. The prediction capabilities of both versions of the above techniques are tested on (i) the ionospheric total electron content (TEC) data, (ii) the daily average rainfall data and (iii) the gamma-ray log data from an offshore oil well, off the west coast of India. The TEC and daily average rainfall data sets designate as examples of data with very high correlations pertaining over very large lags. They also have a strong seasonality component associated with them. However, the gamma-ray log data sets show no seasonality component and have no trends associated with them. Therefore, the choice of different nonlinear data sets having diverse sources of their origins are apt to test the forecasting capabilities of these techniques. It has been observed that W-ANN gives the best prediction, when compared with the other algorithms discussed in this paper. This is believed to be due to the use of non-linear activation function by ANNs to produce regression that results in capturing the inherent non-linear dynamics of the process effectively. The results also show the usefulness of discrete wavelet transform (DWT) coefficients as training features for both linear and non-linear forecasting approaches. The better performance of the wavelet-based forecasting algorithms can be attributed to the fact that DWT coefficients are wide-sense stationary sequences. Thus the wavelet-based versions of these models like W-MMSE and W-Kalman provide better fit to these coefficients, compared to the original time-series data itself. • Wavelet-based forecasting techniques, W-MMSE, W-Kalman and W-ANN are introduced. • W-MMSE, W-Kalman and W-ANN perform better than the regular known techniques. • W-MMSE, W-Kalman and W-ANN are tested on different nonlinear geophysical signals. • W-ANN produces best forecast compared to the other tested wavelet-based techniques. [ABSTRACT FROM AUTHOR]