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Abstract Energy communities are one of the vital puzzle pieces of future smart cities. This paper proposes a novel structure for a sustainable smart city by integrating energy communities in a multi‐carrier energy network. Each energy community has a manager; the so‐called Energy Community Managers (ECM), who trades energy with the upstream Multi‐Carrier Network Operator (MCNO). On the other hand, MCNO participates in the upstream energy markets to satisfy the demands of energy communities by maximizing its own profit. Therefore, ECMs and MCNO should solve a bilevel optimization problem associated with some common variables at both levels such as: energy carrier price and the amount of energy carrier exchange. In fact, MCNO is the leader and ECMs are the followers of such a bilevel optimization problem. Strong duality is employed to convert the bilevel optimization into a single level, while uncertainties are modelled by information gap decision theory and a scenario‐based approach. Sensitivity analysis shows that the thermal energy selling price and the gas buying price are the most crucial influencing on the profit of MCNO by 3.22% and −3.91%, respectively. Furthermore, the obtained results indicate that the risk attitude of the multi‐carrier energy network operator has a critical role in the total profit.

Over the years, detecting stable communities in a complex network has been a major challenge in network science. The global and local structures help to detect communities from different perspectives. However, previous methods based on them suffer from high complexity and fall into local optimum, respectively. The Four-Stage Algorithm (FSA) is proposed to reduce these issues and to allocate nodes to stable communities. Balancing global and local information, as well as accuracy and time complexity, while ensuring the allocation of nodes to stable communities, are the fundamental goals of this research. The Four-Stage Algorithm (FSA) is described and demonstrated using four real-world data with ground truth and three real networks without ground truth. In addition, it is evaluated with the results of seven community detection methods: Three-stage algorithm (TS), Louvain, Infomap, Fastgreedy, Walktrap, Eigenvector, and Label propagation (LPA). Experimental results on seven real network data sets show the effectiveness of our proposed approach and confirm that it is sufficiently capable of identifying those communities that are more desirable. The experimental results confirm that the proposed method can detect more stable and assured communities. For future work, deep learning methods can also be used to extract semantic content features that are more beneficial to investigating networks.

The present study introduces a mechanism design theory framework to address the recruitment problem. Moreover, the study examines the features of the mechanism for the payment function based on value engineering. A two-stage model is also proposed for the mechanism based on the investigation of the initial documentation of the workers at the time of recruitment and their performance over the probationary period. The mechanism takes advantage of the concept of value engineering to account for the critical factors in selecting a firm for collaboration from a worker’s perspective, such as wage and subjective preferences. Various features of the mechanism, such as incentive compatibility (honesty) of the worker and the firm, the individual rationality of the worker and the firm, and the ex-post efficiency of the mechanism were investigated based on the proposed wage by introducing the utility function of each agent. Implementing recruitment in the form of a mechanism revealed that value engineering based pricing is a weighted VCG mechanism, and one of the factors in value engineering success would thus be the practical implementation of an optimized model. Examining recruitment in the simple framework of the mechanism alongside the introduction of the proper structure to study the influence of various features of the worker and the firm indicated that it is possible to implement more complex models such as payment function change, message exchange, and release of some assumptions regarding the workers and the firm.

The aim of this study is to present two time-series forecasting models and combine these models to provide a short-term prediction for hourly electricity demand, using daily electricity consumption data for the period 2006-2011. The first model is based on the decomposition of the electricity load into deterministic and stochastic components and the second model is based on the assumption that the electricity load is a stochastic time series. Once the hourly demand for electricity load is predicted using the above-mentioned models, the performance of the combined model is compared with the two time-series models and also with the dispatching unit model (a multi-variable model in which the weather variable is also included). The results show that the use of the combined model leads to an increase in prediction accuracy over the two time-series models. Moreover, the accuracy of the combined model is as good as the dispatching unit model for most of the time during the day, and even better during the consumption peak hours.