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Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

• Develop an Optimal Resource Selection Framework on Internet-of-Things (ORSF-IoT). • It supports optimal resource selection through the proposed algorithms, namely, Resource Discovery and Improved Firefly Algorithms. • Propose an Iterative Agglomerative Fuzzy K-Means (IAFKM) Clustering Algorithm for selection of best resources by indexing and ranking them on the basis of matched resources. • The framework provides optimal solution for selecting the parametric value having exceeded limit as the brightest firefly in less time. • It helps in providing timely alarms to the system for corrective measurements and enhances system performancesignificantly. The fundamental requirement for communication and computation across distinct application areas on Internet-of-Things is the resource discovery that demands appropriate reasoning for the optimal selection. With exponential growth of resources and their produced huge amount of heterogeneous data, various activities with respect to foraging and sense-making loops face challenges due to interoperability. Hence, interoperability emerges as a major bottleneck for the requirement. Therefore, to eliminate the challenge, the paper has proposed an "Optimal Resource Selection Framework for Internet-of-Things" that deals with the interoperability and ease the resource discovery and selection. The framework facilitates formation of semantic knowledge base as Shared Virtual Composite Ontology for capturing dynamic IoT heterogeneous data. Moreover, it supports optimal resource selection through the proposed algorithms, namely , Resource discovery Algorithm and Improved Firefly Algorithm. Both algorithms target coordination and optimization with Shared Ontology, respectively. The feasibility of the framework is checked against data collected from Sutlej river, Ludhiana, Punjab, India. The proposed framework is evaluated using benchmark functions with respect to metrics such as mean, standard deviation, processing and execution time. The obtained results are compared with the existing Nature-Inspired algorithms to confirm the efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

In this article, a new optimization technique known as Kho-Kho optimization (KKO) algorithm is presented. This proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho. The performance and superiority of the proposed method with respect to other existing methods is evaluated using twenty nine benchmark functions and real-time optimization problems related to power system i.e. combined emission economic dispatch and combined heat and power economic dispatch problem. • This paper presents a new Kho-Kho optimization algorithm (KKO). • KKO is inspired by a well-known tag-team game played in India known as Kho-Kho. • KKO mimics the strategies used by a chasing team to catch the runners of a running team. • Twenty-nine benchmark functions are used to test the performance of the proposed algorithm. • Further, KKO is used to solve real-time optimization problems of power system. [ABSTRACT FROM AUTHOR]