Prediction of Solar Irradiance when having future parameters? What's best AI method could do that?

14 次查看(过去 30 天)
Hello everybody, hope you're doing well all. I'm now working on long-term project which aims to predict Solar irradiance (W/m²) based on several historical parameters. My question is, since weather conditions changes everyday (Humidity parameter as an example), how I'm gonna predict next month Solar Irradiance for a specific day as I still don't know Humidity value for that day next month? What AI tool in Matlab could do that without knowing future Humidity? I'll appreciate your help all. Thanks in advance!

回答(1 个)

Aditya
Aditya 2023-8-30
Hey Amer
I understand your problem statement this answer might help you.
When predicting solar irradiance for a specific day next month without knowing the future humidity value, you can use a technique called time series forecasting. Time series forecasting models can capture patterns and trends in historical data to make predictions for future time points.
In MATLAB, you can use various AI and machine learning tools to perform time series forecasting. Here are a few options you can explore:
1. Autoregressive Integrated Moving Average (ARIMA): ARIMA models are widely used for time series forecasting. They can capture the temporal dependencies and patterns in your historical data. MATLAB provides the `arima` function for fitting ARIMA models and making predictions.
2. Long Short-Term Memory (LSTM) networks: LSTM networks are a type of recurrent neural network (RNN) that can effectively model sequences and time dependencies. They are commonly used for time series forecasting tasks. MATLAB's Deep Learning Toolbox provides functions like `lstmLayer` and `trainNetwork` to build and train LSTM networks.
3. Support Vector Regression (SVR): SVR is a machine learning technique that can be used for time series forecasting. It can capture nonlinear relationships in the data. MATLAB's Statistics and Machine Learning Toolbox provides the `fitrsvm` function for training SVR models.
4. Gaussian Process Regression (GPR): GPR is a probabilistic machine learning method that can model complex relationships in time series data. It provides uncertainty estimates along with predictions. MATLAB's Statistics and Machine Learning Toolbox provides the `fitrgp` function for training GPR models.

产品


版本

R2021a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by