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@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+assets/24-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]cooling.png filter=lfs diff=lfs merge=lfs -text
+assets/24-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]electricity.png filter=lfs diff=lfs merge=lfs -text
+assets/24-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]heating.png filter=lfs diff=lfs merge=lfs -text
+assets/48-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]cooling.png filter=lfs diff=lfs merge=lfs -text
+assets/48-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]electricity.png filter=lfs diff=lfs merge=lfs -text
+assets/48-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]heating.png filter=lfs diff=lfs merge=lfs -text
+assets/72-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]cooling.png filter=lfs diff=lfs merge=lfs -text
+assets/72-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]electricity.png filter=lfs diff=lfs merge=lfs -text
+assets/72-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]heating.png filter=lfs diff=lfs merge=lfs -text
+assets/96-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]cooling.png filter=lfs diff=lfs merge=lfs -text
+assets/96-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]electricity.png filter=lfs diff=lfs merge=lfs -text
+assets/96-steps/the[[:space:]]first[[:space:]]month[[:space:]]pred[[:space:]]heating.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,164 +1,120 @@
----
-license: mit
-pipeline_tag: time-series-forecasting
-metrics:
-- accuracy
-- mae
-- mape
----
-
-
-# Model Card for DMP-PCFC
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-Advanced neural architecture , DMP-PCFC is an interpretable and accurate model for multi-step energy loads prediction in integrated energy systems, 
-or the broader task of time series forecasting.
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-- **Developed by:**  Xingyu Liang and Min Xia.
-- **Model type:**  DMP-PCFC (Dual-Resolution Channel Multi-Period Cross Reconstruction Parallel Closed-Form Continuous-Time Network)
-- **Language(s) (NLP):**  Not applicable 
-- **License:**  MIT License
-- **Finetuned from model [optional]:**  Original implementation
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** https://github.com/nuist-xf/DMP-PCFC
-
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-Energy engineers and researchers can do so directly using the DMP-PCFC framework:
-- Multi-energy load forecasting for integrated energy systems (electricity, cooling, heat)
-- Multi-resolution dynamic capture and long-term trend analysis
-- Interpretable feature interaction analysis based on biological neuron dynamics
-- Predicting sudden changes in energy demand patterns under extreme climate events such as hurricanes and heat waves
-- Provides highly accurate input signals for demand response systems
-
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-- Smart Grid Real-Time Dispatch System
-- Energy consumption optimisation for industrial IoT devices
-- Assessing the carbon reduction potential of renewable energy alternatives
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-- Non-periodic time series forecasting (e.g., sudden event detection)
-- Non-energy sector forecasts (e.g., financial time series)
-- Unstructured data processing such as image/text
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-- Training data limitations: current validation of an IES system based on climatic conditions in Arizona, USA
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-- Recommended for migrated learning in conjunction with local data
-
-## How to Get Started with the Model
-
-All data acquisition, preprocessing, loading, hyperparameters of the model, inference speed, 
-number of parameters and the rest of the relevant content about the experiment have been presented in the repository： https://github.com/nuist-xf/DMP-PCFC
-
-
-## Training Details
-
-### Training Data
-
-More Information Needed
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-### Training Procedure
-
-More Information Needed
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Training Hyperparameters
-
-- **Training regime:** More Information Needed
-  
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-More Information Needed
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-More Information Needed
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-More Information Needed
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-More Information Needed
-
-### Results
-
-More Information Needed
-
-#### Summary
-
-More Information Needed
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** NVIDIA GeForce RTX 3090
-- **Hours used:** More Information Needed
-- **Cloud Provider:** More Information Needed
-- **Compute Region:** More Information Needed
-- **Carbon Emitted:** More Information Needed
-
-
-## Model Card Authors
-
-**Xingyu Liang**
-
-## Model Card Contact
+# DMP-PCFC: Dual-Resolution Channel Multi-Period Cross Reconstruction Parallel Closed-Form Continuous-Time Network
+
+[![Python 3.10](https://img.shields.io/badge/Python-3.10.13-blue?logo=python)](https://www.python.org/)
+[![PyTorch 2.0](https://img.shields.io/badge/PyTorch-2.0.1%2Bcu118-red?logo=pytorch)](https://pytorch.org/)
+[![License MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+
+Advanced neural architecture for multi-step energy loads prediction in integrated energy systems, utilizing data from:
+- [ASU Tempe Campus IES](http://cm.asu.edu/)
+- [NOAA NCEI](https://www.ncei.noaa.gov/)
+
+![Cooling load 24 Hours Prediction Visualization](assets/24-steps/thefirstmonthpredcooling.png)
+
+## 🚀 Getting Started
+
+### Prerequisites
+- ​**NVIDIA GPU**​ with CUDA 11.8 support
+- Python 3.10.13
+- PyTorch 2.0.1
+- numpy 1.26.0
+- pandas 2.0.3
+
+### Installation
+```bash
+# Clone repository
+git clone https://github.com/nuist-xf/DMP-PCFC.git
+cd DMP-PCFC
+
+# Create conda environment
+conda create -n dmp-pcfc python=3.10.13
+conda activate dmp-pcfc
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run Pipeline
+
+# train（Automatic use of the GPU）
+python train.py
+
+# Generate the results of the prediction metrics
+python Save_result_multipredict.py
+```
+```bash
+Main hyperparameters
+# 24 steps
+parser.add_argument('--epochs', type=int, default=39, help='')
+parser.add_argument('--lr', type=float, default=0.008, help='learning rate')
+parser.add_argument('--patience', type=int, default=2, help='patience')
+parser.add_argument('--lr_d', type=float, default=0.5, help='inverse data')
+
+# 48 steps
+# parser.add_argument('--seq_out_len', type=int, default=24*2, help='output sequence length')
+# parser.add_argument('--horizon', type=int, default=24*2)
+# parser.add_argument('--epochs', type=int, default=39, help='')
+# parser.add_argument('--lr', type=float, default=0.008, help='learning rate')
+# parser.add_argument('--patience', type=int, default=1, help='patience')
+# parser.add_argument('--lr_d', type=float, default=0.45, help='inverse data')
+
+# 72 steps
+# parser.add_argument('--seq_out_len', type=int, default=24*3, help='output sequence length')
+# parser.add_argument('--horizon', type=int, default=24*3)
+# parser.add_argument('--epochs', type=int, default=39, help='')
+# parser.add_argument('--lr', type=float, default=0.008, help='learning rate')
+# parser.add_argument('--patience', type=int, default=1, help='patience')
+# parser.add_argument('--lr_d', type=float, default=0.45, help='inverse data')
+
+# 96 steps
+# parser.add_argument('--seq_out_len', type=int, default=24*4, help='output sequence length')
+# parser.add_argument('--horizon', type=int, default=24*4)
+# parser.add_argument('--epochs', type=int, default=39, help='')
+# parser.add_argument('--lr', type=float, default=0.008, help='learning rate')
+# parser.add_argument('--patience', type=int, default=1, help='patience')
+# parser.add_argument('--lr_d', type=float, default=0.45, help='inverse data')
+```
+
+## Repository Structure
+```
+DMP-PCFC/
+├── assets/                 # Visualization resources
+│   ├── 24-steps/          # 24-hour predictions
+│   ├── 48-steps/          # 48-hour predictions
+│   ├── 72-steps/          # 72-hour predictions
+│   └── 96-steps/          # 96-hour predictions
+│
+├── data/                  
+│   └── dataset_input.csv  # Preprocessed sequence data
+│
+├── model/                 # Trained model weights
+│   ├── 24-steps/model.pt  
+│   ├── 48-steps/model.pt  
+│   ├── 72-steps/model.pt  
+│   └── 96-steps/model.pt  
+│
+├── result/                # Prediction results
+│   ├── 24-steps/          
+│   ├── 48-steps/          
+│   ├── 72-steps/          
+│   └── 96-steps/          
+│
+├── metrics.py             # Evaluation metrics (RMSE/MAE/MAPE)
+├── train.py               # Main training script
+├── trainer.py             # Training process management
+├── torch_cfc.py           # DMP-PCFC model implementation
+├── Save_result_multipredict.py  # Full test set evaluation metrics
+├── Save_result.py         # Daily first-moment evaluation metrics
+└── util.py                # Data loading utilities
+```
+
+
+
+## Contact
 For technical support or data access requests:
 - **Liguo Weng**  
   📧 [email protected] 
-  🏛 Nanjing University of Information Science & Technology
+  🏛 Nanjing University of Information Science & Technology
+
+## ⚖️ License
+This project is licensed under the [MIT License](LICENSE) - see the LICENSE file for details.
+```
+

Save_result.py

@@ -0,0 +1,153 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+from metrics import MAE, MAPE, RMSE
+import csv
+
+
+def show_pred(all_y_true, all_predict_values, horizon):
+    # all_y_true = all_y_true.reshape(all_y_true.shape[0], int(math.sqrt(all_y_true.shape[1])), -1)
+    # all_predict_values = all_predict_values.reshape(all_predict_values.shape[0], int(math.sqrt(all_predict_values.shape[1])), -1)
+    header = ['test_y', 'predicted_values']
+    ele_values = np.concatenate((all_y_true[:, 0, :1], all_predict_values[:, 0, :1]), axis=1)
+    with open('./result/ele.csv', 'w', encoding='utf-8', newline='') as fp:
+        # 写
+        writer = csv.writer(fp)
+        # 设置第一行标题头
+        writer.writerow(header)
+        # 将数据写入
+        writer.writerows(ele_values)
+
+    cooling_values = np.concatenate((all_y_true[:, 0, 1:2], all_predict_values[:, 0, 1:2]), axis=1)
+    with open('./result/cooling.csv', 'w', encoding='utf-8', newline='') as fp:
+        # 写
+        writer = csv.writer(fp)
+        # 设置第一行标题头
+        writer.writerow(header)
+        # 将数据写入
+        writer.writerows(cooling_values)
+
+    heating_values = np.concatenate((all_y_true[:, 0, 2:3], all_predict_values[:, 0, 2:3]), axis=1)
+    with open('./result/heating.csv', 'w', encoding='utf-8', newline='') as fp:
+        # 写
+        writer = csv.writer(fp)
+        # 设置第一行标题头
+        writer.writerow(header)
+        # 将数据写入
+        writer.writerows(heating_values)
+
+    mae1 = MAE(all_y_true[:, 0, :1], all_predict_values[:, 0, :1])
+    mape1 = MAPE(all_y_true[:, 0, :1], all_predict_values[:, 0, :1])
+    rmase1 = RMSE(all_y_true[:, 0, :1], all_predict_values[:, 0, :1])
+    predict = all_predict_values[:, 0, :1]
+    Ytest = all_y_true[:, 0, :1]
+    sigma_p = (predict).std(axis=0)
+    sigma_g = (Ytest).std(axis=0)
+    mean_p = predict.mean(axis=0)
+    mean_g = Ytest.mean(axis=0)
+    index = (sigma_g != 0)
+    correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
+    correlation1 = (correlation[index]).mean()
+    print("============电负荷===================")
+    print("MAE = " + str(mae1))
+    print("MAPE = " + str(mape1))
+    print("RMSE = " + str(rmase1))
+    print("acc = " + str(correlation1))
+    print("============cooling==================")
+    mae2 = MAE(all_y_true[:, 0, 1:2], all_predict_values[:, 0, 1:2])
+    mape2 = MAPE(all_y_true[:, 0, 1:2], all_predict_values[:, 0, 1:2])
+    rmase2 = RMSE(all_y_true[:, 0, 1:2], all_predict_values[:, 0, 1:2])
+    predict = all_predict_values[:, 0, 1:2]
+    Ytest = all_y_true[:, 0, 1:2]
+    sigma_p = (predict).std(axis=0)
+    sigma_g = (Ytest).std(axis=0)
+    mean_p = predict.mean(axis=0)
+    mean_g = Ytest.mean(axis=0)
+    index = (sigma_g != 0)
+    correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
+    correlation2 = (correlation[index]).mean()
+    print("MAE = " + str(mae2))
+    print("MAPE = " + str(mape2))
+    print("RMSE = " + str(rmase2))
+    print("acc = " + str(correlation2))
+
+    print("==============heating=================")
+    mae3 = MAE(all_y_true[:, 0, 2:3], all_predict_values[:, 0, 2:3])
+    mape3 = MAPE(all_y_true[:, 0, 2:3], all_predict_values[:, 0, 2:3])
+    rmase3 = RMSE(all_y_true[:, 0, 2:3], all_predict_values[:, 0, 2:3])
+
+    predict = all_predict_values[:, 0, 2:3]
+    Ytest = all_y_true[:, 0, 2:3]
+    sigma_p = (predict).std(axis=0)
+    sigma_g = (Ytest).std(axis=0)
+    mean_p = predict.mean(axis=0)
+    mean_g = Ytest.mean(axis=0)
+    index = (sigma_g != 0)
+    correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
+    correlation3 = (correlation[index]).mean()
+
+    print("MAE = " + str(mae3))
+    print("MAPE = " + str(mape3))
+    print("RMSE = " + str(rmase3))
+    print("acc = " + str(correlation3))
+
+    with open('./result/data.txt', 'w') as f:  # 设置文件对象
+        print("============电负荷===================", file=f, flush=True)
+        print("MAE = " + str(mae1), file=f, flush=True)
+        print("MAPE = " + str(mape1), file=f, flush=True)
+        print("RMSE = " + str(rmase1), file=f, flush=True)
+        print("acc = " + str(correlation1), file=f, flush=True)
+        print("============cooling==================", file=f, flush=True)
+        print("MAE = " + str(mae2), file=f, flush=True)
+        print("MAPE = " + str(mape2), file=f, flush=True)
+        print("RMSE = " + str(rmase2), file=f, flush=True)
+        print("acc = " + str(correlation2), file=f, flush=True)
+        print("==============heating=================", file=f, flush=True)
+        print("MAE = " + str(mae3), file=f, flush=True)
+        print("MAPE = " + str(mape3), file=f, flush=True)
+        print("RMSE = " + str(rmase3), file=f, flush=True)
+        print("acc = " + str(correlation3), file=f, flush=True)
+
+    node_id = 0
+    time = 24 * 31
+    plt.figure(figsize=(20, 10))  # 宽度、高度
+    plt.title("electricity")
+    plt.xlabel("time/one_hour")
+    plt.ylabel("electricity")
+    plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
+    plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
+    plt.legend()
+    plt.savefig("./assets/the first month pred electricity.png")
+    # plt.show()
+
+    node_id = 1
+    time = 24 * 31
+    plt.figure(figsize=(20, 10))  # 宽度、高度
+    plt.title("cooling")
+    plt.xlabel("time/one_hour")
+    plt.ylabel("electricity")
+    plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
+    plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
+    plt.legend()
+    plt.savefig("./assets/the first month pred cooling.png")
+    # plt.show()
+
+    node_id = 2
+    time = 24 * 31
+    plt.figure(figsize=(20, 10))  # 宽度、高度
+    plt.title("heating")
+    plt.xlabel("time/one_hour")
+    plt.ylabel("heating")
+    plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
+    plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
+    plt.legend()
+    plt.savefig("./assets/the first month pred heating.png")
+    # plt.show()
+
+    mae = MAE(all_y_true, all_predict_values)
+    rmse = RMSE(all_y_true, all_predict_values)
+    mape = MAPE(all_y_true, all_predict_values)
+
+    print("ST-GCN基于原始值的精度指标  mae: {:02.4f}, rmse: {:02.4f}, mape: {:02.4f}".format(mae, rmse, mape))
+    with open('./result/data.txt', 'w') as f:  # 设置文件对象
+        print("ST-GCN基于原始值的精度指标  mae: {:02.4f}, rmse: {:02.4f}, mape: {:02.4f}".format(mae, rmse, mape), file=f)

Save_result_multipredict.py

@@ -0,0 +1,188 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+from metrics import MAE, MAPE, RMSE
+import csv
+
+
+
+def show_pred(all_y_true, all_predict_values, horizon):
+    time_steps = horizon
+    # all_y_true = all_y_true.reshape(all_y_true.shape[0], int(math.sqrt(all_y_true.shape[1])), -1)
+    # all_predict_values = all_predict_values.reshape(all_predict_values.shape[0], int(math.sqrt(all_predict_values.shape[1])), -1)
+    header = ['test_y', 'predicted_values']
+    ele_values = np.concatenate((all_y_true[:, 0, :1], all_predict_values[:, 0, :1]), axis=1)
+    # with open('./result/ele.csv', 'a', encoding='utf-8', newline='') as fp:
+    #     # 写
+    #     writer = csv.writer(fp)
+    #     # 设置第一行标题头
+    #     writer.writerow(header)
+    #     # 将数据写入
+    #     writer.writerows(ele_values)
+    #
+    # cooling_values = np.concatenate((all_y_true[:, 0, 1:2], all_predict_values[:, 0, 1:2]), axis=1)
+    # with open('./result/cooling.csv', 'a', encoding='utf-8', newline='') as fp:
+    #     # 写
+    #     writer = csv.writer(fp)
+    #     # 设置第一行标题头
+    #     writer.writerow(header)
+    #     # 将数据写入
+    #     writer.writerows(cooling_values)
+    #
+    # heating_values = np.concatenate((all_y_true[:, 0, 2:3], all_predict_values[:, 0, 2:3]), axis=1)
+    # with open('./result/heating.csv', 'a', encoding='utf-8', newline='') as fp:
+    #     # 写
+    #     writer = csv.writer(fp)
+    #     # 设置第一行标题头
+    #     writer.writerow(header)
+    #     # 将数据写入
+    #     writer.writerows(heating_values)
+
+    def extract_and_concatenate(arr):
+        result = []
+        for feature in range(arr.shape[2]):  # 遍历每个特征
+            # 取样本维度上每隔24个样本的数据
+            extracted = arr[::horizon, :, feature].reshape(-1)
+            result.append(extracted)
+        return result
+
+
+    mae1 = MAE(all_y_true[:, :, :1], all_predict_values[:, :, :1])
+    mape1 = MAPE(all_y_true[:, :, :1], all_predict_values[:, :, :1])
+    rmase1 = RMSE(all_y_true[:, :, :1], all_predict_values[:, :, :1])
+    predict = all_predict_values[:, :, :1]
+    Ytest = all_y_true[:, :, :1]
+    sigma_p = (predict).std(axis=0)
+    sigma_g = (Ytest).std(axis=0)
+    mean_p = predict.mean(axis=0)
+    mean_g = Ytest.mean(axis=0)
+    index = (sigma_g != 0)
+    correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
+    correlation1 = (correlation[index]).mean()
+    print("============整个测试集电负荷===================")
+    print("MAE = " + str(mae1))
+    print("MAPE = " + str(mape1))
+    print("RMSE = " + str(rmase1))
+    print("acc = " + str(correlation1))
+
+    print("============整个测试集cooling==================")
+    mae2 = MAE(all_y_true[:, :, 1:2], all_predict_values[:, :, 1:2])
+    mape2 = MAPE(all_y_true[:, :, 1:2], all_predict_values[:, :, 1:2])
+    rmase2 = RMSE(all_y_true[:, :, 1:2], all_predict_values[:, :, 1:2])
+    predict = all_predict_values[:, :, 1:2]
+    Ytest = all_y_true[:, :, 1:2]
+    sigma_p = (predict).std(axis=0)
+    sigma_g = (Ytest).std(axis=0)
+    mean_p = predict.mean(axis=0)
+    mean_g = Ytest.mean(axis=0)
+    index = (sigma_g != 0)
+    correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
+    correlation2 = (correlation[index]).mean()
+    print("MAE = " + str(mae2))
+    print("MAPE = " + str(mape2))
+    print("RMSE = " + str(rmase2))
+    print("acc = " + str(correlation2))
+
+    print("==============整个测试集heating=================")
+    mae3 = MAE(all_y_true[:, :, 2:3], all_predict_values[:, :, 2:3])
+    mape3 = MAPE(all_y_true[:, :, 2:3], all_predict_values[:, :, 2:3])
+    rmase3 = RMSE(all_y_true[:, :, 2:3], all_predict_values[:, :, 2:3])
+
+    predict = all_predict_values[:, :, 2:3]
+    Ytest = all_y_true[:, :, 2:3]
+    sigma_p = (predict).std(axis=0)
+    sigma_g = (Ytest).std(axis=0)
+    mean_p = predict.mean(axis=0)
+    mean_g = Ytest.mean(axis=0)
+    index = (sigma_g != 0)
+    correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
+    correlation3 = (correlation[index]).mean()
+    print("MAE = " + str(mae3))
+    print("MAPE = " + str(mape3))
+    print("RMSE = " + str(rmase3))
+    print("acc = " + str(correlation3))
+
+    # with open('./result/data.txt', 'a') as f:  # 设置文件对象
+    #     print("============电负荷===================", file=f, flush=True)
+    #     print("MAE = " + str(mae1), file=f, flush=True)
+    #     print("MAPE = " + str(mape1), file=f, flush=True)
+    #     print("RMSE = " + str(rmase1), file=f, flush=True)
+    #     print("acc = " + str(correlation1), file=f, flush=True)
+    #     print("============cooling==================", file=f, flush=True)
+    #     print("MAE = " + str(mae2), file=f, flush=True)
+    #     print("MAPE = " + str(mape2), file=f, flush=True)
+    #     print("RMSE = " + str(rmase2), file=f, flush=True)
+    #     print("acc = " + str(correlation2), file=f, flush=True)
+    #     print("==============heating=================", file=f, flush=True)
+    #     print("MAE = " + str(mae3), file=f, flush=True)
+    #     print("MAPE = " + str(mape3), file=f, flush=True)
+    #     print("RMSE = " + str(rmase3), file=f, flush=True)
+    #     print("acc = " + str(correlation3), file=f, flush=True)
+
+    y_true_extracted = extract_and_concatenate(all_y_true)
+    predict_values_extracted = extract_and_concatenate(all_predict_values)
+
+
+
+
+    node_id = 0
+   # time = 24 * 31
+    plt.figure(figsize=(20, 10))  # 宽度、高度
+    plt.title("electricity")
+    plt.xlabel("time/one_hour")
+    plt.ylabel("electricity")
+    # plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
+    # plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
+    plt.plot(y_true_extracted[node_id], linewidth=1.0, label='true')
+    plt.plot(predict_values_extracted[node_id], linewidth=1.0, label='pred')
+    plt.legend()
+    plt.savefig("./assets/the first month pred electricity.png")
+    # plt.show()
+
+    node_id = 1
+   # time = 24 * 31
+    plt.figure(figsize=(20, 10))  # 宽度、高度
+    plt.title("cooling")
+    plt.xlabel("time/one_hour")
+    plt.ylabel("electricity")
+    # plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
+    # plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
+    plt.plot(y_true_extracted[node_id], linewidth=1.0, label='true')
+    plt.plot(predict_values_extracted[node_id], linewidth=1.0, label='pred')
+    plt.legend()
+    plt.savefig("./assets/the first month pred cooling.png")
+    # plt.show()
+
+    node_id = 2
+   # time = 24 * 31
+    plt.figure(figsize=(20, 10))  # 宽度、高度
+    plt.title("heating")
+    plt.xlabel("time/one_hour")
+    plt.ylabel("heating")
+    # plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
+    # plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
+    plt.plot(y_true_extracted[node_id], linewidth=1.0, label='true')
+    plt.plot(predict_values_extracted[node_id], linewidth=1.0, label='pred')
+    plt.legend()
+    plt.savefig("./assets/the first month pred heating.png")
+    # plt.show()
+
+    mae = MAE(all_y_true, all_predict_values)
+    rmse = RMSE(all_y_true, all_predict_values)
+    mape = MAPE(all_y_true, all_predict_values)
+
+    print("ST-GCN基于原始值的精度指标  mae: {:02.4f}, rmse: {:02.4f}, mape: {:02.4f}".format(mae, rmse, mape))
+    # with open('./result/data.txt', 'a') as f:  # 设置文件对象
+    #     print("ST-GCN基于原始值的精度指标  mae: {:02.4f}, rmse: {:02.4f}, mape: {:02.4f}".format(mae, rmse, mape), file=f)
+    y_true_concatenated = np.concatenate(y_true_extracted)
+    predict_values_concatenated = np.concatenate(predict_values_extracted)
+    mae2 = MAE(y_true_concatenated, predict_values_concatenated)
+    rmse2 = RMSE(y_true_concatenated, predict_values_concatenated)
+    mape2 = MAPE(y_true_concatenated, predict_values_concatenated)
+
+    print("ST-GCN拼接后的精度指标  mae: {:02.4f}, rmse: {:02.4f}, mape: {:02.4f}".format(mae2, rmse2, mape2))
+
+
+all_y_true_loaded_np = np.load('./result/all_y_true.npy')
+all_predict_value_loaded_np = np.load('./result/all_predict_value.npy')
+show_pred(all_y_true_loaded_np, all_predict_value_loaded_np, 24)

assets/24-steps/README.md

@@ -0,0 +1 @@
+

assets/48-steps/README.md

@@ -0,0 +1 @@
+

assets/72-steps/README.md

@@ -0,0 +1 @@
+

assets/96-steps/README.MD

@@ -0,0 +1 @@
+

data/README.md

@@ -0,0 +1 @@
+

metrics.py

@@ -0,0 +1,28 @@
+import math
+
+import numpy as np
+
+
+def MAE(y_true, y_pre):
+    y_true = (y_true).reshape((-1, 1))
+    y_pre = (y_pre).reshape((-1, 1))
+    re = np.abs(y_true - y_pre).mean()
+    return re
+
+
+def RMSE(y_true, y_pre):
+    y_true = (y_true).reshape((-1, 1))
+    y_pre = (y_pre).reshape((-1, 1))
+    re = math.sqrt(((y_true - y_pre) ** 2).mean())
+    return re
+
+
+def MAPE(y_true, y_pre):
+    y_true = (y_true).reshape((-1, 1))
+    y_pre = (y_pre).reshape((-1, 1))
+
+    # e = (y_true + y_pre) / 2 + 1e-2
+    # re = (np.abs(y_true - y_pre) / (np.abs(y_true) + e)).mean()
+    re = np.mean(np.abs((y_true - y_pre) / y_true)) * 100
+
+    return re

model/24-steps/README.md

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+

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requirements.txt

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+# Core dependencies
+torch==2.0.1+cu118  # https://download.pytorch.org/whl/torch_stable.html
+pandas==2.0.3
+numpy==1.26.0
+scikit-learn==1.5.2
+matplotlib==3.7.2

result/24-steps/README.md

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+

result/24-steps/README.txt

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+Prediction Results

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+ST-GCN����ԭʼֵ�ľ���ָ��  mae: 598.4344, rmse: 1026.3455, mape: 5.4302
+Number of model parameters is 13787
+| end of epoch   1 | time:  8.98s | train_mape_loss 8.5714 | train_mae_loss 1220.2565 | valid mae 1137.2440 | valid mape 5.5031 | valid corr  0.9016  learning rate  0.008000
+test mae 719.5639 | test mape 6.3724 | test corr 0.9422

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result/48-steps/data.txt

@@ -0,0 +1 @@
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