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Accurate and reliable prediction of airconditioning load plays a significant role in prosumer energy management system (EMS), because airconditioning load accounts for a large proportion of the building's total energy consumption. This paper proposes a new meta ensemble learning method to realize short-term prediction of airconditioning load for prosumers. This method is a hybrid of meta ensemble learning and stacked auto-encoder (SAE). First, we design multiple different forecasting structures based on SAE to achieve point prediction of airconditioning loads. SAE is used to learn the deep features in airconditioning load data. Second, a new meta ensemble learning prediction model is proposed. Meta ensemble learning is used to learn the nonlinear features and invariant structures in data, and determine the coefficients of each SAE-based point forecaster. Finally, the prediction results of each point forecaster are aggregated and integrated to estimate the final airconditioning load prediction result. Airconditioning load data from a commercial building in Singapore are used to validate the feasibility and effectiveness of the proposed method, demonstrating that the proposed meta ensemble learning method is attractive in prosumer energy management. INDEX TERMS Airconditioning load, load prediction, meta learning, ensemble learning, prosumer energy management system.

Split air conditioner (SAC) plays a critical role for temperature control of buildings. In recent years, the manufacturing process of SAC tends to consider energy-saving and environmental protection factors to reduce air pollution and alleviate energy crisis. However, the design of environmental-friendly SAC will increase the manufacturing cost, thereby increasing the burden on airconditioning companies. Therefore, the eco-cost analysis of SAC is of great significance for air conditioner manufacturers. To this end, a new SAC eco-cost assessment method based on activity based costing method is innovatively proposed in this paper. In this method, a K-nearest neighbor algorithm is used to deal with the missing data in SAC samples. An environmental and non-environmental cost assessment approach is developed based on the activity-based costing method to estimate the eco-cost of SAC over its whole life cycle. In addition, the impact of energy efficiency deviation on SAC eco-cost is numerically analyzed. Finally, the proposed ecocost assessment method is programmed on a Gabi software, and validated using real SAC data from a Chinese airconditioning company. Simulation results show that the proposed method can provide an estimate of the eco-cost of SAC, thus helping airconditioning companies to improve their competitiveness in the future where energy-saving and emission reduction are increasingly important. INDEX TERMS Eco-cost analysis, split air conditioner, life cycle assessment, activity-based costing method, Gabi software.

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