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. Accurate solar and wind power forecasting is important for managing renewable energy systems and maintaining a reliable power supply. Since solar and wind generation changes with weather and environmental conditions, predicting future power output can help improve grid planning, energy management, and storage operation. In this project, LSTM (Long Short-Term Memory) and LSBoost (Least-Squares Boosting) models are developed in MATLAB to forecast solar and wind power generation. Historical generation and weather-related data can be used to train the models and predict future renewable power output. The forecasting results from both methods can then be compared using suitable performance metrics to determine which approach provides better prediction accuracy for different renewable-energy conditions.
Methodology
The project starts by collecting and preprocessing historical solar and wind power data, along with relevant parameters such as irradiance, temperature, wind speed, and direction where available. The dataset is divided into training and testing sets. An LSTM network is developed in MATLAB to learn the time-dependent patterns in renewable power generation. In parallel, an LSBoost regression model is trained using selected input features to predict future solar and wind power. Both models are tested using unseen data, and their predictions are compared with the actual power output. Performance is evaluated using metrics such as RMSE, MAE, and R². Finally, the forecasting results are plotted and compared to identify the strengths of LSTM and LSBoost for renewable-energy prediction.
Verified MATLAB Simulation Code Demonstration
Syntax-highlighted executable code demonstration for Solar and Wind Power Forecasting Using LSTM and LSBoost in MATLAB:
% Spectral FFT Analysis & Signal Filtering
clc; clear; close all;
Fs = 1000; T = 1/Fs; L = 1500; t = (0:L-1)*T;
S = 0.7*sin(2*pi*50*t) + sin(2*pi*120*t);
X = S + 2*randn(size(t));
% Compute Fast Fourier Transform (FFT)
Y = fft(X);
P2 = abs(Y/L);
P1 = P2(1:L/2+1);
P1(2:end-1) = 2*P1(2:end-1);
f = Fs*(0:(L/2))/L;
fprintf('FFT Spectral Analysis Computed Successfully!\n');