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The large ownership of direct-expansion (DX) air-conditioning (A/C) systems in small and medium-sized buildings brings with it the need to reduce their energy consumption without damaging the thermal comfort of the occupants. Model predictive control (MPC) is an effective method to optimally control the operation of air-conditioners. However, most existing MPC methods require the investment of additional equipment and labor-intensive work, which greatly increases the cost of MPC and hinders its practical application. To solve the problem, this paper presents an economical and practical MPC algorithm for DX A/C systems, capable of achieving a balance between thermal comfort and energy saving. The proposed algorithm was experimentally validated on both an experimental DX A/C system and a market available split-type air-conditioner. Experimental results on the experimental DX A/C system show that temperature and humidity set-points selected at α = 1 saved 23.3% of energy consumption compared to those selected at α = 0, while keeping indoor thermal comfort within acceptable range. And results on the split-type air-conditioner demonstrate energy savings of up to more than 32% compared to the baseline and proved that the algorithm can be practically applied on market available D/X air-conditioners. • A practical application-oriented model predictive control algorithm is developed for DX A/C systems. • A cooling capacity prediction ANN model of the DX A/C system is used to predict the cooling load. • An ANN model for predicting the operational status of the DX A/C system is developed without increasing the training data. • A cost function is used to balance indoor thermal comfort levels and energy consumption. • The algorithm has been validated in both an experimental DX A/C system and a market available split-type air-conditioner. [ABSTRACT FROM AUTHOR]