Focusing on the electricity and thermal energy requirement of contemporary buildings, a joint operation of photovoltaic thermal (PV/T)‐based prosumers and a microturbine‐based combined heat and power system has been presented to analyse the economics of a grid‐connected microgrid (MG) system. The bidirectional flow of the electricity and heat model is considered and is optimally managed using a price‐based demand response (DR) scheme. Thermal storage is exploited to ward off the substantial amount of heat wastages that enhance the system's reliability during any disruption of microturbine. The objective functions of both the prosumer and MG operator (MGO) are formulated as a profit maximisation problem where they interact with each other on the basis of DR activity. To establish this strategic decision‐making process, the system is modelled as a Stackelberg equilibrium game, where MGO acts as a leader while PV/T prosumers act as a follower. The interaction or contribution of two players in a game is a problem of non‐linear optimisation, which is solved by the differential evolution algorithm. In the end, in a case study, it has been proved that the results are quite lucrative for the proposed model.

Constructing the power curve of a power generation facility integrated with complex and large-scale industrial processes is a difficult task but can be accomplished using Industry 4.0 data analytics tools. This research attempts to construct the data-driven power curve of the generator installed at a 660 MW power plant by incorporating artificial intelligence (AI)-based modeling tools. The power produced from the generator is modeled by an artificial neural network (ANN)—a reliable data analytical technique of deep learning. Similarly, the R2.ai application, which belongs to the automated machine learning (AutoML) platform, is employed to show the alternative modeling methods in using the AI approach. Comparatively, the ANN performed well in the external validation test and was deployed to construct the generator's power curve. Monte Carlo experiments comprising the power plant’s thermo-electric operating parameters and the Gaussian noise are simulated with the ANN, and thus the power curve of the generator is constructed with a 95% confidence interval. The performance curves of industrial systems and machinery based on their operational data can be constructed using ANNs, and the decisions driven by these performance curves could contribute to the Industry 4.0 vision of effective operation management.

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