Soft Computing (SC) is a hot topic in Artificial Intelligence in which mathematical problems and modeling play a key role. SC techniques can be used to tackle problems characterized byimprecision, uncertainty, and partial truth to achieve tractability and robustness at a low computational cost. For this reason, SC techniques can obtain approximate solutions to problems which have no known methods to compute an exact solution. SC methods draw on the problem solving strategies that can be found in natural domains, like evolution, which differ fundamentally from the mathematically based computing methods normally used in engineering The main SC paradigms include Fuzzy Systems, Evolutionary Computation, Artificial Neural Computing, Metaheuristics, and Swarm Intelligence.

The abovementioned features render SC particularly suitable for analyzing data produced by smart buildings, i.e., buildings equipped with the latest technological and architectural solutions, controlled by Building Management Systems (BMS), operating in fulfillment of the typical goals of increasing occupants’ comfort and reducing buildings’ energy consumption.

BMSs usually produce a high volume of data, and such data is typically characterized by imprecision and the presence of noise. Moreover, SC techniques allow easy integration of human knowledge, which can help achieve better solutions when analyzing the large amount of data generated by the building management systems. This data can then be processed to obtain insights into the behavior of smart buildings.

The overall aim of this special issue is to compile the latest research and development, up-to-date issues, mathematical aspects, and challenges in the field of SC and its applications to the analysis of data produced by smart buildings.

Potential topics include but are not limited to the following:

• Time series forecasting for smart building data
• Real time data analytics
• Efficient data management
• Numerical approach for safety aspects of buildings
• Obtaining mathematical models with Soft Computing strategies
• Mathematical tools for Soft Computing techniques
• Theoretical and numerical methods for Soft Computing
• Mathematical tools for Soft Computing
• Mathematical tools suitable for the analysis of Soft Computing techniques
• Stochastic and dynamic environments, noisy evaluations
• Complexity theory for search heuristics
• Statistical approaches