Update README.md

README.md

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 ---
 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
+For technical support or data access requests:
+- **Liguo Weng**  
+  📧 [email protected] 
+  🏛 Nanjing University of Information Science & Technology