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🔥 Content Introduction
1. Introduction: Core Requirements and Model Adaptability of Multi-Input Single-Output Regression
In industrial time series prediction scenarios (such as photovoltaic power, power load, and equipment energy consumption prediction), “multi-input single-output” is a typical requirement. For example, in photovoltaic power prediction, it is necessary to combine solar irradiance, ambient temperature, relative humidity, wind speed, and historical power as multi-dimensional input variables to accurately predict the photovoltaic power for the next hour / 24 hours (single output). This type of task faces two core challenges:
- Coexistence of multi-variable coupling and temporal dependence: There is a nonlinear coupling between input variables (e.g., increased irradiance + increased temperature jointly affect power), and the data exhibits periodicity over time (daily irradiance shows a “bell-shaped” variation). A single model (such as pure LSTM or pure Adaboost) struggles to handle both “local fluctuation features” and “long-term temporal dependencies” simultaneously;
- Insufficient generalization and uncertainty interference: New energy data is significantly affected by meteorological changes (such as cloud cover) and sensor noise, leading traditional deep learning models to easily overfit. Ensemble learning can reduce prediction variance through the fusion of multiple base learners.
The CNN-LSTM-Adaboost combined model precisely addresses the above pain points:
- CNN (1D Convolution): Extracts local temporal features of multiple input variables (e.g., sudden increases in irradiance, short-term fluctuations in temperature);
- LSTM: Captures long-term temporal dependencies among multiple variables (e.g., the influence of the irradiance trend over the past 6 hours on current power);
- Adaboost: Uses the prediction results of CNN-LSTM as input for base learners, correcting prediction bias through “weighted integration” to enhance model generalization and anti-interference capabilities.
This article relies on publicly available photovoltaic datasets (NASA SSE + actual measurement data from a photovoltaic power station in China) to fully dissect the modeling process of multi-input single-output regression, providing a reusable framework for similar time series prediction tasks.
2. Core Technology Analysis: Collaborative Logic of Multi-Input Single-Output Regression Models
(1) Definition of Multi-Input Variables and Scenario Adaptation
Taking “future 1-hour photovoltaic power prediction” as an example, we clarify the input-output system (in line with the essence of multi-input single-output regression):
|
Variable Type |
Specific Variable |
Physical Meaning |
Data Dimension (Time Series Length) |
|
Multi-Input Variables |
Solar Irradiance (G) |
Solar radiation energy received per unit area (W/m²) |
Time series data from the past 24 hours |
|
Ambient Temperature (T) |
Ambient temperature around the photovoltaic module (°C) |
Time series data from the past 24 hours |
|
|
Relative Humidity (H) |
Proportion of water vapor in the air (%) |
Time series data from the past 24 hours |
|
|
Wind Speed (v) |
Ambient wind speed (m/s) |
Time series data from the past 24 hours |
|
|
Historical Photovoltaic Power (P_prev) |
Historical output of the photovoltaic power station (kW) |
Time series data from the past 24 hours |
|
|
Single Output Variable |
Future 1-hour Photovoltaic Power (P_pred) |
Actual output during the prediction period (kW) |
Single value output |
(2) CNN-LSTM: Core Extractor of Multi-Variable Temporal Features
1. CNN Layer: Local Coupling Feature Extraction (Adapting to Multi-Input Variables)
For the 24-hour time series data of 5 input variables, we adopt1D multi-channel convolution design to simultaneously process local correlations of multiple variables:
⛳️ Running Results
📣 Sample Code
output=data(:,13)’;
nwhole =size(data,1);
% Shuffle dataset
% temp=randperm(nwhole);
% Do not shuffle dataset
temp=1:nwhole;
train_ratio=0.9;
ntrain=round(nwhole*train_ratio);
ntest =nwhole-ntrain;
% Prepare input and output training data
input_train =input(:,temp(1:ntrain));
output_train=output(:,temp(1:ntrain));
% Prepare test data
input_test =input(:, temp(ntrain+1:ntrain+ntest));
output_test=output(:,temp(ntrain+1:ntrain+ntest));
T_train=output_train;
T_test=output_test;
%% Data normalization
method=@mapminmax;
[inputn_train,inputps]=method(input_train);
inputn_test=method(‘apply’,input_test,inputps);
[outputn_train,outputps]=method(output_train);
outputn_test=method(‘apply’,output_test,outputps);
% Create cell or vector, length equal to training set size;
XrTrain = cell(size(inputn_train,2),1);
YrTrain = zeros(size(outputn_train,2),1);
for i=1:size(inputn_train,2)
XrTrain{i,1} = inputn_train(:,i);
YrTrain(i,1) = outputn_train(:,i);
end
% Create cell or vector, length equal to test set size;
XrTest = cell(size(inputn_test,2),1);
YrTest = zeros(size(outputn_test,2),1);
for i=1:size(input_test,2)
XrTest{i,1} = inputn_test(:,i);
YrTest(i,1) = outputn_test(:,i);
end
M=size(inputn_train,2);
N=size(inputn_test,2);
🔗 References
🎈 Some theoretical references are from online literature; if there is any infringement, please contact the author for removal.
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