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The consumption of air conditioners accounts for nearly even more than 50% of all the loads of a building, but the air conditioner setting can be optimized within the thermal comfort range to reduce the demand. This paper proposes a customized air conditioner control method for every single family from the perspective of a large number of residential users with different requirements. It can meet the thermal preference and develop an appropriate control air conditioners strategy for each user through the power and environmental data which will not affect their cost and privacy. Combined with this method, a proportional control strategy is presented to optimize the energy consumption, which can also solve the inaccurate human parameters problem caused by difficult acquisition. Finally, case studies demonstrate that the proposed method is more suitable for residential users, which can fully tap the response potential of users with high heat resistance and avoid the reduction and fluctuations of response capacity by the exit of users with low heat resistance due to discomfort.

Accurate and timely ultra-short-term wind farm cluster power forecasting is significant for real-time dispatch and frequency regulation of power grids. Distinguishing different types of power fluctuation patterns based on fluctuation process analysis and training prediction models separately based on pattern partitioning results, is beneficial for improving the prediction accuracy of wind farm cluster power. However, existing pattern partitioning methods have a single perspective and have not yet formed a multi-dimensional evaluation routine to quantify the fluctuation characteristics of different patterns. Furthermore, for wind farm clusters, there is a lack of consideration of the dynamic spatio-temporal relationship between adjacent wind farm stations under different power fluctuation patterns. To make up for these deficiencies, this article proposes an ultra-short-term wind farm cluster power forecasting model based on power fluctuation pattern recognition and spatio-temporal graph neural network pattern prediction. First, the extreme points are statistically analyzed, and the wind farm cluster power is divided into different fluctuation processes. Then four indicators are summarized from the time stationarity and amplitude volatility of these fluctuation processes to guide the partition of power fluctuation patterns. Finally, considering the dynamic spatio-temporal correlation between adjacent stations under various fluctuation patterns, the spatio-temporal graph neural network is exploited for model training for each fluctuation pattern. After identifying the fluctuation patterns of the wind power series in the test set, the corresponding trained model is used to obtain the final prediction results. Experiments with other benchmarks show that the proposed method is superior on real wind farm cluster power dataset.

Wind power curtailment is of great importance with the increase of large-scale wind power connected to the grid. A new concept of redundant wind power accommodated by dispatching electric water heaters (EWHs) is developed in the paper. Precise predictions of wind power and EWHs load power are the basis for this work. A hybrid multi-kernel prediction approach integrating an adaptive fruit fly optimization algorithm (AFOA) and multi-kernel relevance vector machine (MKRVM) is proposed to deal with the sample distribution of multi-source heterogeneous features uncovered by an energy entropy method, where AFOA is used to determine the kernel parameters in MKRVM adaptively and avoid the arbitrariness. For the large computation of the prediction approach, parallel computation based on the Hadoop cluster is used to accelerate the calculation. Then, an economic dispatching model for accommodating wind power is built taking into account the penalty of curtailed wind power and the operating cost of EWHs. The proposed scheme is implemented in an intelligent residential district. The results show that the optimization performance of the hybrid prediction approach is superior to those of four usual optimization algorithms in this case. Regular or orderly scheduling of EWHs enables accommodation of superfluous wind power and reduces dispatch cost.