The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency. By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation. This paper is an in-depth review of recent studies on the application of artificial intelligence (AI) technologies in smart buildings through the concept of a building management system (BMS) and demand response programs (DRPs). In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing the recent research conducted in this field and across the major AI domains, including energy, comfort, design, and maintenance. Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings.

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In the traditional electricity market, the safety of power systems due to increasing power demand, especially in peakvalley, is seriously affected. In recent years, the smart electricity market's development, and the implementation of demand response programs (DRPs) have resulted in electricity price reduction, improving loads with demand flexibility, and enhancing security by interacting between customers and the market. The main aim of this study is an optimization model of price-responsive demand in the wholesale market in Japan considering the concept of dynamic price elasticity of demand, single and multi-period loads, and customer benefit function. The proposed model is founded on the theories of price and customer in microeconomics which can help decision-makers in the electricity market evaluate the customers' behaviors based on the decision variables like the price of electricity and customers' participation level and incentives. The customer benefit function is derived based on the self and crossprice elasticities of demand and single and multi-period loads. Moreover, different Time-Based Programs (TBP) of DRPs are applied to the model to evaluate the effect of changing the price on customers' demand. DRPs are prioritized using Multi Criteria Decesion Matrix (MCDM) technique in orderto help decision makers for prioritizing different DRPs to improve load profile features. The developed model is tested in the next step, using day-ahead spot price from the Japan Electric Power Exchange (JEPX) and demand loads in Wholesale Market in Japan.

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