用AlexNet和LSTM结合预测电价，准确率超97%。
Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
- 融合AlexNet特征提取与LSTM时序建模，提升预测能力。
- 在真实数据上达97.08%准确率，优于RNN（96.64%）和ANN（96.63%）。
- 适用于考虑温度、光照、降雨等外部因素的电价预测场景。
近年来，先进机器学习方法在混合模型中的发展显著提升了电力价格预测的准确性。本文提出一种结合AlexNet与LSTM的新型混合模型，用于更精确地预测电价。尽管传统RNN和ANN在某些场景下有效，但难以处理外汇时间序列数据，且仅关注供需关系，忽略了外部影响因素。本模型引入需求、温度、日照和降雨等关键变量，通过最小-最大归一化和时间窗口技术对历史数据建模。实验结果表明，该混合模型在预测精度上优于独立使用的RNN（96.64%）和ANN（96.63%），达到97.08%的准确率，显著提升预测性能。
原文摘要 · Abstract (English)
The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively.
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