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In the present work, India's primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values (1971–2014). Energy use is analysed as an optimisation problem, where a unique ensemble of two metaheuristic algorithms, Grammatical Evolution (GE), and Differential Evolution (DE), is applied. The energy optimisation problem has been investigated in two ways: estimation and a year-ahead prediction. Models are compared using RMSE (objective function) and further ranked using the Global Performance Index (GPI). For the estimation problem, RMSE values are found to be as low as 0.0078 and 0.0103 on training and test datasets, respectively. The average estimated energy use is found in good agreement with the data (RMSE = 6.3749 kgoe/capita), and the best model (E10) has an RMSE of 5.8183 kgoe/capita, with a GPI of 1.7249. For the prediction problem, RMSE is found to be 0.0096 and 0.0122 on training and test datasets, respectively. The average predicted energy use has RMSE of 7.8857 (kgoe/capita), while Model P20 has the best value of RMSE (7.9201 kgoe/capita) and a GPI of 1.8836. [ABSTRACT FROM AUTHOR]

The negative public opinions and views on overseas direct investment (ODI) of a multinational enterprise (MNE) will damage the image of its brand and are likely to bring it serious economic and social losses. So, it is important for the MNE to understand the formation and spread mechanism of public opinion risk (POR) in order to effectively respond to and guide the public opinion. This research proposed a multifractal-based situation optimization method to explore the POR evolution based on the media-based negative sentiment on China's ODI. The sentiment measurement is obtained by a directed crawler for gathering the text of media reports corresponding to a certain ODI event using a URL knowledge base from the GDELT Event Database. Taking the public opinion crisis of the tax evasion incident of the local arm of China's MNE in India as an example, the experiments show that this method could dynamically monitor the POR event in real-time and help MNE guide the effective control and benign evolution of public opinion of the event. [ABSTRACT FROM AUTHOR]

The Electricity Demand Prediction (EDP) and Generation Expansion Planning (GEP) make a real-world planning problem more optimal. In this study, the EDP problem has been solved using the optimization techniques such as Differential Evolution (DE), Genetic Algorithm (GA) and Artificial Immune System (AIS) for Tamil Nadu state, India. The electricity demand has been predicted and validated in terms of Mean Absolute Percentage Error (MAPE). The results reveal that DE outperformed other algorithms in solving the EDP problem. Continually, the GEP problem under partial deregulated environmental has been solved using the DE. In the GEP model, utilities and Independent Power Producer (IPP) have acted as the generating sources where IPP sells the power to utilities and utilities sell the power to the consumers. The utilities have been modeled to ensure the profit of all IPP, fuel mix ratio, system security, reliability, etc. The problem has been solved for the 6-year (till 2024) and 12-year (till 2030) planning horizons by considering three different scenarios, such as (i) without budget constraint of utilities, (ii) with budget constraint of utilities and (iii) penetration of huge Renewable Energy Source (RES) with budget constraint of utilities. The expansion capacities of each power plants were estimated over the planning horizon by maximizing the profit to the generating sources. The values of reliability indices and CO2 emission have been obtained. [ABSTRACT FROM AUTHOR]

The first case of COVID‐19 in India detected on January 30, 2020, after its emergence in Wuhan, China, in December 2019. The lockdown was imposed as anemergency measure by the Indian government to prevent the spread of COVID‐19 but gradually eased out due to its vast economic consequences. Just 15 days after the relaxation of lockdown restrictions, Delhi became India's worst city in terms of COVID‐19 cases. In this paper, we propose a variable‐order fractional SIR (susceptible, infected, removed) model at state‐level scale. We introduce a algorithm that uses the differential evolution algorithm in combination with Adam–Bashforth–Moulton method to learn the parameters in a system of variable‐order fractional SIR model. The model can predict the confirm COVID‐19 cases in India considering the effects of nationwide lockdown and the possible estimate of the number of infliction inactive cases after the removal of lockdown on June 1, 2020. A new parameter p is introduced in the classical SIR model representing the fraction of infected people that get tested and are thereby quarantined. The COVID‐19 trajectory in Delhi, as per our model, predicts the slowing down of the spread between January and February 2021, touching a peak of around 5 lakh confirmed cases. [ABSTRACT FROM AUTHOR]

Transmission-constrained generation expansion planning (TC-GEP) problem for new generating units significantly involves location, capacity and type of fuel. This problem can be solved by adding Optimal Power Flow (OPF) constraints. This study renders an application of the Self-adaptive Differential Evolution (SaDE) algorithm to the TC-GEP problem for multiple horizons, at least cost, for the power generating system of Tamil Nadu, India. TC-GEP problem has been solved for 6-year (till 2022) and 12-year (till 2028) planning horizon by considering the least cost and reliable supply. The problem is solved for six different scenarios on an Indian utility 62 bus test system, and the results are validated with Dynamic Programming (DP). The results of the TC-GEP problem for the year 2028 are compared with the solutions of the GEP problem without transmission constraint. Finally, a comparison is made between the proposed solution and the practically implemented expansion plan for the year 2017 by the Tamil Nadu electricity sector. [ABSTRACT FROM AUTHOR]

The focus of this study is to examine the service quality of unsignalized intersections based on the perception of automobile drivers. This was achieved by developing Automobile Level of Service (ALOS) models using two computational intelligence methods – Functional Linked Artificial Neural Network (FLANN) and Differential Evolution (DE). The required data were collected at 47 unsignalized intersections in India with widely varying driving environments. Traffic simulation models were developed to estimate parameters that could not be directly measured in the field. DE model exhibited higher prediction efficiencies with coefficient of determination (R2) of 0.94 and 0.93 for training and testing datasets, respectively. Applying sensitivity analysis, significant parameters affecting ALOS were arranged in descending rank of their relative influence. Pavement condition is the most significant parameter influencing ALOS of unsignalized intersections. The proposed model will help the transportation administrators in prioritizing the key factors for investment in infrastructural development. [ABSTRACT FROM AUTHOR]

• A novel approach is proposed to detect the non-stationarity in rainfall time series. • Developed a probabilistic time-dependent bivariate intensity-duration model. • Proposed approach checks for any signature of non-stationarity in the probabilistic distribution parameters and prunes off the insignificant (stationary) probabilistic distribution parameters, thus reducing the complexity and improving the computational efficiency of the model. • The present approach is superior to the stationary and traditional non-stationary approaches in avoiding any possible under/over-estimation of the return periods. Dynamic behavior of extreme rainfall characteristics heightened by the abrupt warming of the environment has affected the sustainability of the existing water resources systems and infrastructure, which were designed employing the traditional 'stationary' assumption. Here, we propose a realistic and efficient framework to detect non-stationarity in the observed hydrologic variables, to overcome a few limitations suffered by the traditional non-stationary approaches. The methodology is demonstrated over the short period rainfall series of four major metropolitan cities of India, where the intensity of the rainfall is reportedly increasing sharply accompanied by the changes in the pattern of rainfall. Since direct runoff is influenced by the intensity and duration of rainfall, it is important to study the joint characteristics of intensity and duration in the context of non-stationarity, especially in urban regions where the relationship is more distinct. Hence, we estimated the time-varying joint return period/return level of its extremes, utilizing a dynamic Bayesian copula. We have implemented time-varying multivariate probability frequency analysis to derive the time-varying Intensity-Duration relationship. Here we employed a Bayesian approach through Differential Evolution Markov Chain (DE–MC) algorithm to estimate the uncertainty bound of the time-varying return level. The results emphasize that the probabilistic distribution parameters vary both temporally and spatially, and recommend the incorporation of non-stationarity in the extreme event modeling, only if there is a change in the probabilistic distribution parameters. This non-stationary model can be seamlessly employed to compute return levels with better accuracy and reliability than traditional stationary/non-stationary methods. We observe that the short duration return level increases at a faster rate than the longer ones, with the credibility interval larger than that of long duration return levels. The results also highlight the importance of adopting event based non-stationary IDF curves for the design of water resource systems and to devise long-term decision-making strategies to address the changing climate. However, we do not draw any conclusion concerning climate change, in general, from the short duration rainfall records used in this study. [ABSTRACT FROM AUTHOR]

In India, power shortage is a major issue for economical growth. According to the data provided by National Load Despatch Centre, peak power shortage in Tamilnadu during the year 2014 varies between 3000 MW and 4000 MW. This power shortage can be reduced by increasing installed capacity of conventional and non-conventional energy sources. But the construction of new generation plants is cost-effective and also power generation is not assured throughout the year. This power shortage can also be minimized by implementing load management in the consumer side. This paper focuses on the reduction of peak demand by the proper operating schedule of major equipments. For this analysis, three rice industries have been considered. The major operating sections in the rice industries are pre-cleaning, soaking, pre-milling and milling. In this proposed work, to reduce the peak demand, the operating time of pre-milling section is rescheduled using the optimization techniques, DE (Differential Evolution), PSO (Particle Swarm Optimization) and ABC (Artificial Bee Colony). The rescheduled results given by DE, PSO and ABC algorithms reduce the peak demand cost of the energy consumed in three rice industries. However, the optimum scheduling obtained by ABC reduces the feeder power flow than the DE and PSO scheduling. [ABSTRACT FROM AUTHOR]

Coal mills are bottleneck in coal–fired power generation process due to difficulty in developing efficient controls and faults occurring inside the mills. In this paper, a dynamic coal mill model is developed using conservation laws and empirical relations. The physical processes occurring inside the mill such as grinding, pneumatic transport, drying, heat transfer, and classification are modeled. Differential Evolution is used to estimate the model parameters. The model is validated using data obtained from a 500 MW plant in India. An application of the proposed model for online estimation of moisture in the raw coal is presented. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient functional link artificial neural network (CEFLANN) based adaptive model for financial time series prediction of leading Indian stock market indices. Financial time-series data are usually non-stationary and volatile in nature. The proposed adaptive CEFLANN based model employs the least mean square (LMS) algorithm with a new cost function to train the weights of the networks. The mean absolute percentage error (MAPE) with respect to actual stock prices is selected as the performance index to estimate the quality of prediction. The CEFLANN model inputs are chosen from the past stock prices of different market sectors along with technical indicators to determine best stock trend prediction one day ahead in time. Further to improve the performance of the CEFLANN model, weights are optimized using an adaptive differential evolution (DE) algorithm and its overall prediction performance is compared with the improved LMS algorithm showing the effectiveness of the DE in producing more accurate forecast. We have selected different combinations of important technical indicators to have a strong control on changes in stock indices. [ABSTRACT FROM AUTHOR]