随着“双碳”战略的深入推进和新能源渗透率的持续攀升，构建7天时间尺度的新能源功率预测体系，已成为东北电网应对其运行特性的现实且迫切的需求。然而，以多层感知机（MLP）为代表的线性网络和以长短时记忆网络（LSTM）为代表的循环神经网络模型在处理长序列复杂依赖关系时，普遍存在梯度消失与并行化困难等问题，制约了预测精度的进一步提升。为此，本文提出了一种哈里斯鹰优化算法（HHO）与Transformer架构相结合的混合模型，通过HHO对Transformer架构中的模型维度、编码器层数等关键超参数进行优化，提升模型的泛化能力与预测稳定性。实验以MLP与LSTM为基线模型，采用东北地区某风电场及光伏电站全年发电功率数据，从平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）三个指标对模型性能进行综合评价。结果显示，在风电功率7天预测中，本文所提模型相较于LSTM在MAE、RMSE和MAPE上分别提升21.7%、14.1%与10.2%；在光伏功率7天预测中，三项指标分别提升34.3%、40.9%和23.8%。结果充分验证了该模型在7天时间尺度预测中的优越性能，为高比例新能源接入背景下电网的可靠运行提供了有效的技术支撑。

Under the “Dual Carbon” strategy, the increasing penetration of renewable energy has prompted the Northeast China Power Grid, based on its specific operational characteristics, to raise the practical need for renewable power forecasting at a 7-day time scale. However, conventional models such as the Multilayer Perceptron (MLP) and recurrent neural networks represented by the Long Short-Term Memory (LSTM) often suffer from gradient vanishing and parallelization difficulties when capturing complex long-range dependencies in sequences, which limits further improvement in forecasting performance. To address these issues, this paper proposes a hybrid model that integrates the Harris Hawks Optimization (HHO) algorithm with the Transformer architecture. By using HHO to optimize key hyperparameters in the Transformer, such as model dimension and the number of encoder layers, the model’s generalization capability and forecasting stability are enhanced. Experiments is conducted using annual power generation data from a wind farm and a photovoltaic power station in Northeast China, with MLP and LSTM selected as baseline models. A comprehensive evaluation is performed based on three metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that in 7-day wind power forecasting, the proposed model achieves improvements of 21.7%, 14.1%, and 10.2% in MAE, RMSE, and MAPE, respectively, compared to LSTM. For 7-day photovoltaic power forecasting, the corresponding improvements over LSTM are 34.3%, 40.9%, and 23.8%. These findings fully validate the superior performance of the proposed model in 7-day time-scale forecasting, providing effective technical support for the reliable operation of power grids with high penetration of renewable energy.