基于状态频率记忆网络的家庭短期电力负荷预测

Short-term Household Load Forecasting Based on State Frequency Memory Network

家庭的短期电力负荷预测在智能电网中发挥着越来越重要的作用,为了进一步提高预测的精度,提出了一种基于状态频率记忆网络的家庭短期电力负荷预测模型。首先采用K均值聚类方法,将具有相同用电模式的家庭归为一类;随后采用小波降噪技术对负荷数据进行降噪处理;最后构建状态频率记忆网络模型进行批量的家庭负荷预测。该模型通过引入离散傅里叶变换将记忆状态分解为多个频率分量,并通过这些频率成分的组合来预测未来的用电量。使用均方误差、均方根误差和平均绝对误差来评估模型,与该领域上性能表现最好的长短期记忆模型相比较,文中的模型在未来一天的负荷预测中,3类误差分别降低了21.6%、11.4%、15.4%,充分验证了模型的有效性。

Short-term household power load forecasting has played an increasing important role in smart grid. To further improve the accuracy of the forecasting, a state frequency memory network-based short-term household power load forecasting model was proposed. Firstly, the k-means clustering method was utilized to classify the families possessing the same electricity consumption mode into the same category. Secondly,the wavelet denoising technology was applied to the load data.Finally, a state frequency memory network model was constructed to perform batch of household power load forecasting.In the proposed model, the discrete Fourier transform was led in to decompose the memory state into multi frequency components, and by means of the combination of these frequency components the future electricity consumption was forecasted.The mean square error(abbr. MSE), root mean square error(abbr. RMSE) and mean absolute error(abbr. MAE) were used to evaluate the proposed model. Taking the load forecasting of the next day for example, comparing the results obtained by LSTM, which behaves the best in this field, with those obtained by the proposed model, the error of forecasted results of three kinds of household power load has reduced by 21.6%,11.4% and 15.4% respectively, thus, the effectiveness of the proposed model is fully verified.