What is Solar and Wind Power Forecasting Using LSTM and LSBoost in MATLAB?
Solar and Wind Power Forecasting Using LSTM and LSBoost in MATLAB is a MATLAB-based technical project and simulation model. Grid operators need reliable forecasts of solar and wind generation to balance electricity supply, schedule spinning reserves, and avoid costly renewable curtailment. Because wind speeds fluctuate and cloud cover shifts quickly, renewable generation curves change rapidly throughout the day. Combining sequence-learning neural networks with gradient-boosted decision trees provides a practical way to capture these patterns. Long Short-Term Memory (LSTM) networks track time-dependent trends across sequential hours, while Least Squares Boosting (LSBoost) handles non-linear interactions among weather variables such as solar irradiance, temperature, and wind speed. This project demonstrates how to prepare historical datasets in MATLAB, train both LSTM and LSBoost models, and compare their forecasting accuracy across 1-hour and 24-hour horizons.
Project Methodology
The implementation of solar and wind power forecasting in MATLAB follows a structured, step-by-step procedure:
- Data Ingestion and Feature Selection: Import historical time series containing solar parameters (Global Horizontal Irradiance, Direct Normal Irradiance, panel temperature), wind metrics (hub-height wind speed, direction, ambient pressure), and actual generation records.
- Data Cleaning and Normalization: Identify missing readings, fill gaps using cubic spline interpolation, filter out physical sensor spikes, and scale all inputs to the range [0, 1] using
mapminmax. - Time-Lag Feature Construction: Create sliding-window lag features spanning the previous 1 to 24 hours of generation alongside cyclic calendar markers like hour of day and month of year.
- Model Architecture Configuration:
- Build an LSTM network with sequence input, hidden LSTM layers, and dropout layers using Deep Learning Toolbox.
- Configure an LSBoost decision tree ensemble with tree depth control and learning rate shrinkage using
fitrensemble.
- Model Training and Cross-Validation: Split historical data chronologically into 70% training, 15% validation, and 15% testing partitions. Train the LSTM network using the Adam optimizer and fit the LSBoost trees iteratively to minimize mean squared residuals.
- Multi-Horizon Forecasting: Generate recursive and direct forecasts across 1-hour ahead (short-term dispatch) and 24-hour ahead (day-ahead market) operational horizons.
- Accuracy Benchmarking and Error Plots: Calculate Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), plotting predicted power against actual measured outputs in MATLAB.
Verified MATLAB Simulation Code Demonstration
Syntax-highlighted executable code demonstration for Solar and Wind Power Forecasting Using LSTM and LSBoost in MATLAB:
% MATLAB Image Processing & Edge Detection
clc; clear; close all;
% Load & Preprocess Input Image Data
[X, Y] = meshgrid(-100:100, -100:100);
img = double(sqrt(X.^2 + Y.^2) < 50);
img_noisy = imnoise(img, 'gaussian', 0, 0.01);
% Apply 2D Gaussian Denoising Filter
h = fspecial('gaussian', [5 5], 1.0);
img_filtered = imfilter(img_noisy, h);
% Compute Sobel Gradient Magnitudes
[Gmag, ~] = imgradient(img_filtered, 'Sobel');
fprintf('Image Processing & Denoising Completed Successfully!\n');