电力系统短期负荷预测
1. 数据准备和预处理
classdef LoadDataPreprocessor
properties
raw_data
processed_data
feature_names
temporal_features
weather_features
historical_features
end
methods
function obj = LoadDataPreprocessor(data_file)
% 构造函数
if nargin > 0
obj = obj.load_data(data_file);
end
end
function obj = load_data(obj, data_file)
% 加载电力负荷数据
fprintf('加载数据文件: %s\n', data_file);
% 支持多种数据格式
[~, ~, ext] = fileparts(data_file);
switch lower(ext)
case '.csv'
obj.raw_data = readtable(data_file);
case '.mat'
loaded_data = load(data_file);
obj.raw_data = loaded_data.data;
case {'.xlsx', '.xls'}
obj.raw_data = readtable(data_file);
otherwise
error('不支持的文件格式');
end
fprintf('数据加载完成，共 %d 行，%d 列\n', ...
size(obj.raw_data, 1), size(obj.raw_data, 2));
end
function obj = preprocess_data(obj)
% 数据预处理流程
fprintf('开始数据预处理...\n');
% 1. 处理缺失值
obj = obj.handle_missing_values();
% 2. 异常值检测和处理
obj = obj.handle_outliers();
% 3. 特征工程
obj = obj.feature_engineering();
% 4. 数据标准化
obj = obj.normalize_data();
fprintf('数据预处理完成\n');
end
function obj = handle_missing_values(obj)
% 处理缺失值
data = obj.raw_data;
% 检查缺失值
missing_ratio = sum(ismissing(data)) / height(data);
fprintf('缺失值比例:\n');
disp(missing_ratio);
% 对数值列使用插值
numeric_vars = varfun(@isnumeric, data, 'OutputFormat', 'uniform');
numeric_data = data(:, numeric_vars);
for i = 1:width(numeric_data)
if missing_ratio(i) < 0.3 % 如果缺失值比例小于30%
numeric_data{:,i} = fillmissing(numeric_data{:,i}, 'linear');
else
numeric_data{:,i} = fillmissing(numeric_data{:,i}, 'constant', 0);
end
end
data(:, numeric_vars) = numeric_data;
obj.raw_data = data;
end
function obj = handle_outliers(obj)
% 异常值处理
data = obj.raw_data;
% 假设负荷数据在第一个数值列
load_data = data{:,1};
% 使用IQR方法检测异常值
Q1 = quantile(load_data, 0.25);
Q3 = quantile(load_data, 0.75);
IQR = Q3 - Q1;
lower_bound = Q1 - 1.5 * IQR;
upper_bound = Q3 + 1.5 * IQR;
outliers = load_data < lower_bound | load_data > upper_bound;
fprintf('检测到 %d 个异常值 (%.2f%%)\n', ...
sum(outliers), sum(outliers)/length(load_data)*100);
% 使用移动中位数替换异常值
if sum(outliers) > 0
load_data_clean = load_data;
for i = 1:length(load_data)
if outliers(i)
% 使用前后各2个小时的数据计算中位数
start_idx = max(1, i-2);
end_idx = min(length(load_data), i+2);
neighbor_data = load_data(start_idx:end_idx);
valid_neighbors = neighbor_data(~outliers(start_idx:end_idx));
if ~isempty(valid_neighbors)
load_data_clean(i) = median(valid_neighbors);
else
load_data_clean(i) = median(load_data(~outliers));
end
end
end
data{:,1} = load_data_clean;
end
obj.raw_data = data;
end
function obj = feature_engineering(obj)
% 特征工程
data = obj.raw_data;
% 提取时间特征
if isdatetime(data{:,1}) || iscell(data{:,1})
time_col = data{:,1};
if iscell(time_col)
time_col = datetime(time_col);
end
% 提取多种时间特征
data.Hour = hour(time_col);
data.DayOfWeek = weekday(time_col);
data.Month = month(time_col);
data.DayOfYear = day(time_col, 'dayofyear');
data.IsWeekend = isweekend(time_col);
data.Season = obj.get_season(month(time_col));
% 节假日特征
data.IsHoliday = obj.is_holiday(time_col);
% 滞后特征
data.Load_Lag1 = [NaN; data{1:end-1, 2}]; % 假设第二列是负荷
data.Load_Lag24 = [NaN(24,1); data{1:end-24, 2}];
data.Load_Lag168 = [NaN(168,1); data{1:end-168, 2}];
% 移动平均特征
data.Load_MA24 = movmean(data{:,2}, [23, 0], 'omitnan');
data.Load_MA168 = movmean(data{:,2}, [167, 0], 'omitnan');
obj.temporal_features = {'Hour', 'DayOfWeek', 'Month', ...
'DayOfYear', 'IsWeekend', 'Season', 'IsHoliday'};
obj.historical_features = {'Load_Lag1', 'Load_Lag24', ...
'Load_Lag168', 'Load_MA24', 'Load_MA168'};
end
obj.processed_data = data;
obj.feature_names = data.Properties.VariableNames;
end
function season = get_season(~, month)
% 根据月份获取季节
season = zeros(size(month));
season(month >= 3 & month <= 5) = 1; % 春季
season(month >= 6 & month <= 8) = 2; % 夏季
season(month >= 9 & month <= 11) = 3; % 秋季
season(month == 12 | month <= 2) = 4; % 冬季
end
function is_holiday = is_holiday(~, dates)
% 简单的节假日判断（需要根据实际情况完善）
is_holiday = false(size(dates));
% 这里可以添加具体的节假日判断逻辑
% 例如：is_holiday = (month(dates) == 1 & day(dates) == 1); % 元旦
end
function obj = normalize_data(obj)
% 数据标准化
data = obj.processed_data;
% 对数值列进行标准化
numeric_vars = varfun(@isnumeric, data, 'OutputFormat', 'uniform');
for i = 1:length(numeric_vars)
if numeric_vars(i)
col_data = data{:,i};
if ~all(isnan(col_data))
% Z-score标准化
mu = mean(col_data, 'omitnan');
sigma = std(col_data, 'omitnan');
if sigma > 0
data{:,i} = (col_data - mu) / sigma;
end
end
end
end
obj.processed_data = data;
end
function [X_train, X_test, y_train, y_test] = split_data(obj, test_ratio, lag_hours)
% 分割数据集
if nargin < 2
test_ratio = 0.2;
end
if nargin < 3
lag_hours = 24;
end
data = obj.processed_data;
numeric_data = data{:, varfun(@isnumeric, data, 'OutputFormat', 'uniform')};
numeric_data = rmmissing(numeric_data); % 移除包含NaN的行
% 准备特征和目标变量
X = numeric_data(:, 2:end); % 假设第一列是时间，第二列开始是特征
y = numeric_data(:, 1); % 假设第一列是负荷值
% 分割数据
n = size(X, 1);
split_point = floor(n * (1 - test_ratio));
X_train = X(1:split_point, :);
X_test = X(split_point+1:end, :);
y_train = y(1:split_point);
y_test = y(split_point+1:end);
fprintf('训练集: %d 样本, 测试集: %d 样本\n', ...
size(X_train, 1), size(X_test, 1));
end
end
end
2. 多种预测模型实现
classdef LoadForecaster
properties
models
model_names
predictions
actual_values
performance_metrics
feature_importance
end
methods
function obj = LoadForecaster()
% 构造函数
obj.models = struct();
obj.model_names = {};
obj.predictions = struct();
obj.performance_metrics = struct();
end
function obj = train_arima(obj, X_train, y_train, order)
% ARIMA模型训练
fprintf('训练ARIMA模型...\n');
if nargin < 4
order = [2, 1, 2]; % [p, d, q]
end
try
% 确保数据是列向量
y_train = y_train(:);
% 创建ARIMA模型
Mdl = arima('ARLags', 1:order(1), ...
'D', order(2), ...
'MALags', 1:order(3), ...
'Constant', 0);
% 训练模型
obj.models.arima = estimate(Mdl, y_train, 'Display', 'off');
obj.model_names{end+1} = 'ARIMA';
fprintf('ARIMA模型训练完成\n');
catch ME
fprintf('ARIMA训练错误: %s\n', ME.message);
end
end
function obj = train_svm(obj, X_train, y_train, kernel_type)
% SVM模型训练
fprintf('训练SVM模型...\n');
if nargin < 4
kernel_type = 'gaussian';
end
try
% 训练SVM回归模型
if strcmpi(kernel_type, 'gaussian')
obj.models.svm = fitrsvm(X_train, y_train, ...
'KernelFunction', 'gaussian', ...
'Standardize', true, ...
'KernelScale', 'auto');
else
obj.models.svm = fitrsvm(X_train, y_train, ...
'KernelFunction', kernel_type, ...
'Standardize', true);
end
obj.model_names{end+1} = 'SVM';
fprintf('SVM模型训练完成\n');
catch ME
fprintf('SVM训练错误: %s\n', ME.message);
end
end
function obj = train_random_forest(obj, X_train, y_train, num_trees)
% 随机森林训练
fprintf('训练随机森林模型...\n');
if nargin < 4
num_trees = 100;
end
try
% 训练随机森林
obj.models.random_forest = TreeBagger(num_trees, X_train, y_train, ...
'Method', 'regression', ...
'OOBPrediction', 'on', ...
'MinLeafSize', 5);
obj.model_names{end+1} = 'RandomForest';
% 计算特征重要性
obj.feature_importance.random_forest = ...
obj.models.random_forest.OOBPermutedPredictorDeltaError;
fprintf('随机森林训练完成，OOB误差: %.4f\n', ...
mean(obj.models.random_forest.oobError));
catch ME
fprintf('随机森林训练错误: %s\n', ME.message);
end
end
function obj = train_xgboost(obj, X_train, y_train, params)
% XGBoost模型训练（需要安装XGBoost库）
fprintf('训练XGBoost模型...\n');
if nargin < 4
params = struct('max_depth', 6, 'learning_rate', 0.1, ...
'n_estimators', 100, 'objective', 'reg:squarederror');
end
try
% 转换数据为XGBoost格式
dtrain = obj.mat2xgb(X_train, y_train);
% 训练模型
obj.models.xgboost = xgboost_train(params, dtrain, ...
params.n_estimators, struct('verbose', 0));
obj.model_names{end+1} = 'XGBoost';
fprintf('XGBoost模型训练完成\n');
catch ME
fprintf('XGBoost训练错误: %s\n', ME.message);
fprintf('请确保已安装XGBoost for MATLAB\n');
end
end
function obj = train_lstm(obj, X_train, y_train, lstm_params)
% LSTM模型训练
fprintf('训练LSTM模型...\n');
if nargin < 4
lstm_params = struct('hidden_units', 50, 'num_layers', 2, ...
'epochs', 50, 'learning_rate', 0.001);
end
try
% 准备LSTM数据
[X_lstm, y_lstm] = obj.prepare_lstm_data(X_train, y_train, 24);
% 创建LSTM网络
layers = [ ...
sequenceInputLayer(size(X_lstm, 2))
lstmLayer(lstm_params.hidden_units, 'OutputMode', 'last')
fullyConnectedLayer(50)
reluLayer()
fullyConnectedLayer(1)
regressionLayer()];
options = trainingOptions('adam', ...
'MaxEpochs', lstm_params.epochs, ...
'InitialLearnRate', lstm_params.learning_rate, ...
'Verbose', false);
% 训练网络
obj.models.lstm = trainNetwork(X_lstm, y_lstm, layers, options);
obj.model_names{end+1} = 'LSTM';
fprintf('LSTM模型训练完成\n');
catch ME
fprintf('LSTM训练错误: %s\n', ME.message);
end
end
function [X_seq, y_seq] = prepare_lstm_data(~, X, y, sequence_length)
% 准备LSTM序列数据
num_sequences = size(X, 1) - sequence_length;
X_seq = cell(num_sequences, 1);
y_seq = zeros(num_sequences, 1);
for i = 1:num_sequences
X_seq{i} = X(i:i+sequence_length-1, :)';
y_seq(i) = y(i+sequence_length);
end
end
function predictions = predict_arima(obj, X_test, y_test, steps)
% ARIMA模型预测
if nargin < 4
steps = length(y_test);
end
if isfield(obj.models, 'arima')
try
[y_pred, y_mse] = forecast(obj.models.arima, steps, 'Y0', y_test(1:min(100, length(y_test))));
predictions = y_pred;
catch
predictions = zeros(steps, 1);
end
else
predictions = zeros(steps, 1);
end
end
function predictions = predict_svm(obj, X_test)
% SVM模型预测
if isfield(obj.models, 'svm')
predictions = predict(obj.models.svm, X_test);
else
predictions = zeros(size(X_test, 1), 1);
end
end
function predictions = predict_random_forest(obj, X_test)
% 随机森林预测
if isfield(obj.models, 'random_forest')
predictions = predict(obj.models.random_forest, X_test);
else
predictions = zeros(size(X_test, 1), 1);
end
end
function obj = predict_all_models(obj, X_test, y_test)
% 所有模型预测
fprintf('开始模型预测...\n');
obj.actual_values = y_test;
% 各模型预测
for i = 1:length(obj.model_names)
model_name = obj.model_names{i};
fprintf('使用 %s 模型进行预测...\n', model_name);
switch lower(model_name)
case 'arima'
pred = obj.predict_arima(X_test, y_test, length(y_test));
case 'svm'
pred = obj.predict_svm(X_test);
case 'randomforest'
pred = obj.predict_random_forest(X_test);
case 'xgboost'
pred = obj.predict_xgboost(X_test);
case 'lstm'
pred = obj.predict_lstm(X_test);
otherwise
pred = zeros(length(y_test), 1);
end
obj.predictions.(model_name) = pred;
% 计算性能指标
obj.performance_metrics.(model_name) = ...
obj.calculate_metrics(y_test, pred);
end
end
function metrics = calculate_metrics(~, y_true, y_pred)
% 计算预测性能指标
metrics = struct();
% 移除NaN值
valid_idx = ~isnan(y_true) & ~isnan(y_pred);
y_true = y_true(valid_idx);
y_pred = y_pred(valid_idx);
if isempty(y_true)
metrics.MAE = NaN;
metrics.RMSE = NaN;
metrics.MAPE = NaN;
metrics.R2 = NaN;
return;
end
% 平均绝对误差
metrics.MAE = mean(abs(y_true - y_pred));
% 均方根误差
metrics.RMSE = sqrt(mean((y_true - y_pred).^2));
% 平均绝对百分比误差
metrics.MAPE = mean(abs((y_true - y_pred) ./ y_true)) * 100;
% R²决定系数
SS_res = sum((y_true - y_pred).^2);
SS_tot = sum((y_true - mean(y_true)).^2);
metrics.R2 = 1 - (SS_res / SS_tot);
end
function plot_predictions(obj, model_names)
% 绘制预测结果
if nargin < 2
model_names = obj.model_names;
end
figure('Position', [100, 100, 1200, 800]);
% 预测结果对比
subplot(2, 2, 1);
time_points = 1:length(obj.actual_values);
plot(time_points, obj.actual_values, 'k-', 'LineWidth', 2);
hold on;
colors = lines(length(model_names));
legend_entries = {'实际值'};
for i = 1:length(model_names)
model_name = model_names{i};
if isfield(obj.predictions, model_name)
pred = obj.predictions.(model_name);
plot(time_points, pred, '--', 'Color', colors(i,:), 'LineWidth', 1.5);
legend_entries{end+1} = model_name;
end
end
xlabel('时间点');
ylabel('负荷值');
title('负荷预测结果对比');
legend(legend_entries, 'Location', 'best');
grid on;
% 误差分布
subplot(2, 2, 2);
errors = [];
error_labels = {};
for i = 1:length(model_names)
model_name = model_names{i};
if isfield(obj.predictions, model_name)
pred = obj.predictions.(model_name);
error = abs(pred - obj.actual_values);
errors = [errors; error];
error_labels = [error_labels; repmat({model_name}, length(error), 1)];
end
end
boxplot(errors, error_labels);
ylabel('绝对误差');
title('各模型误差分布');
grid on;
% 性能指标对比
subplot(2, 2, 3);
metrics = {'MAE', 'RMSE', 'MAPE'};
metric_data = zeros(length(metrics), length(model_names));
for i = 1:length(model_names)
model_name = model_names{i};
if isfield(obj.performance_metrics, model_name)
for j = 1:length(metrics)
metric_data(j, i) = obj.performance_metrics.(model_name).(metrics{j});
end
end
end
bar(metric_data');
set(gca, 'XTickLabel', model_names);
ylabel('指标值');
title('模型性能指标对比');
legend(metrics, 'Location', 'best');
grid on;
% 特征重要性（随机森林）
subplot(2, 2, 4);
if isfield(obj.feature_importance, 'random_forest')
importance = obj.feature_importance.random_forest;
[~, idx] = sort(importance, 'descend');
top_idx = idx(1:min(10, length(importance)));
barh(importance(top_idx));
set(gca, 'YTickLabel', top_idx);
xlabel('重要性得分');
title('随机森林特征重要性 (Top 10)');
grid on;
end
end
function print_performance(obj)
% 打印性能指标
fprintf('\n=== 模型性能比较 ===\n');
fprintf('%-15s %-8s %-8s %-8s %-8s\n', ...
'模型', 'MAE', 'RMSE', 'MAPE%', 'R²');
fprintf('%-15s %-8s %-8s %-8s %-8s\n', ...
'-----', '---', '----', '-----', '--');
for i = 1:length(obj.model_names)
model_name = obj.model_names{i};
if isfield(obj.performance_metrics, model_name)
m = obj.performance_metrics.(model_name);
fprintf('%-15s %-8.3f %-8.3f %-8.2f %-8.3f\n', ...
model_name, m.MAE, m.RMSE, m.MAPE, m.R2);
end
end
end
end
end
3. 集成学习和模型融合
classdef EnsembleForecaster
properties
base_models
ensemble_weights
ensemble_method
predictions
performance
end
methods
function obj = EnsembleForecaster(base_models, method)
% 构造函数
obj.base_models = base_models;
if nargin < 2
obj.ensemble_method = 'weighted_average';
else
obj.ensemble_method = method;
end
end
function obj = train_ensemble(obj, X_train, y_train, X_val, y_val)
% 训练集成模型
fprintf('训练集成预测模型...\n');
% 获取基模型的预测
base_predictions = [];
model_weights = [];
for i = 1:length(obj.base_models)
model = obj.base_models{i};
% 获取模型在验证集上的预测
if isa(model, 'LoadForecaster')
pred = model.predict_all_models(X_val, y_val);
% 这里需要根据具体实现调整
else
pred = predict(model, X_val);
end
base_predictions = [base_predictions, pred];
% 根据性能计算权重
mae = mean(abs(pred - y_val));
weight = 1 / (mae + eps); % 避免除零
model_weights = [model_weights, weight];
end
% 归一化权重
obj.ensemble_weights = model_weights / sum(model_weights);
fprintf('集成模型训练完成，权重: %s\n', mat2str(obj.ensemble_weights, 2));
end
function ensemble_pred = predict_ensemble(obj, X_test)
% 集成预测
base_preds = zeros(size(X_test, 1), length(obj.base_models));
for i = 1:length(obj.base_models)
model = obj.base_models{i};
if isa(model, 'LoadForecaster')
% 处理LoadForecaster对象的预测
pred = model.predict_all_models(X_test, zeros(size(X_test,1),1));
else
pred = predict(model, X_test);
end
base_preds(:, i) = pred;
end
switch obj.ensemble_method
case 'weighted_average'
ensemble_pred = base_preds * obj.ensemble_weights';
case 'median'
ensemble_pred = median(base_preds, 2);
case 'best_model'
[~, best_idx] = max(obj.ensemble_weights);
ensemble_pred = base_preds(:, best_idx);
otherwise
ensemble_pred = mean(base_preds, 2);
end
end
end
end
4. 完整的使用示例
% 电力系统短期负荷预测主程序
function main_short_term_load_forecasting()
% 清除环境
clear; close all; clc;
fprintf('=== 电力系统短期负荷预测系统 ===\n\n');
% 1. 数据准备和预处理
fprintf('步骤1: 数据准备和预处理\n');
% 创建示例数据（实际应用中替换为真实数据）
[X, y] = create_sample_load_data();
% 数据预处理
preprocessor = LoadDataPreprocessor();
preprocessor.raw_data = array2table([y, X], 'VariableNames', ...
{'Load', 'Feature1', 'Feature2', 'Feature3', 'Feature4'});
preprocessor = preprocessor.preprocess_data();
% 分割数据
[X_train, X_test, y_train, y_test] = preprocessor.split_data(0.2);
% 2. 训练预测模型
fprintf('\n步骤2: 训练预测模型\n');
forecaster = LoadForecaster();
% 训练多个模型
forecaster = forecaster.train_arima(X_train, y_train);
forecaster = forecaster.train_svm(X_train, y_train);
forecaster = forecaster.train_random_forest(X_train, y_train);
% 3. 模型预测和评估
fprintf('\n步骤3: 模型预测和评估\n');
forecaster = forecaster.predict_all_models(X_test, y_test);
% 显示性能比较
forecaster.print_performance();
% 4. 可视化结果
fprintf('\n步骤4: 结果可视化\n');
forecaster.plot_predictions();
% 5. 集成学习
fprintf('\n步骤5: 集成学习\n');
% 创建集成模型（示例）
base_models = {forecaster.models.svm, forecaster.models.random_forest};
ensemble = EnsembleForecaster(base_models, 'weighted_average');
% 这里需要验证集数据来训练集成权重
% ensemble = ensemble.train_ensemble(X_train, y_train, X_val, y_val);
fprintf('\n=== 预测完成 ===\n');
end
% 创建示例数据函数
function [X, y] = create_sample_load_data()
% 创建示例负荷数据
rng(42); % 设置随机种子确保可重现
n_points = 1000;
time = (1:n_points)';
% 基础负荷模式
base_load = 1000 + 500 * sin(2*pi*time/24) + 200 * sin(2*pi*time/168);
% 添加天气影响
temperature = 20 + 10 * sin(2*pi*time/24) + 5 * randn(n_points, 1);
humidity = 50 + 20 * sin(2*pi*time/12) + 10 * randn(n_points, 1);
% 添加随机噪声
noise = 50 * randn(n_points, 1);
% 生成目标负荷
y = base_load + 10 * temperature + 5 * humidity + noise;
% 生成特征
X = [temperature, humidity, sin(2*pi*time/24), cos(2*pi*time/24)];
fprintf('生成示例数据: %d 个时间点\n', n_points);
end
% 实时预测函数
function real_time_forecasting_example()
% 实时预测示例
fprintf('实时负荷预测示例...\n');
% 加载最新数据
% 这里应该是从实时数据库或API获取数据
recent_data = get_recent_load_data();
% 使用训练好的模型进行预测
% load('trained_forecaster.mat'); % 加载已训练的模型
% 进行未来24小时预测
horizon = 24;
% future_pred = predict_future(forecaster, recent_data, horizon);
fprintf('未来%d小时负荷预测完成\n', horizon);
end
5. 性能优化和高级功能
% 模型超参数优化
function optimized_forecaster = optimize_hyperparameters(X_train, y_train)
% 使用贝叶斯优化寻找最佳超参数
fprintf('开始超参数优化...\n');
% 定义优化变量
optim_vars = [
optimizableVariable('NumTrees', [50, 200], 'Type', 'integer');
optimizableVariable('MinLeafSize', [1, 20], 'Type', 'integer');
optimizableVariable('MaxDepth', [5, 20], 'Type', 'integer')
];
% 目标函数
objective_function = @(params) train_evaluate_model(X_train, y_train, params);
% 贝叶斯优化
results = bayesopt(objective_function, optim_vars, ...
'MaxTime', 3600, 'Verbose', 1, ...
'PlotFcn', {@plotObjectiveModel, @plotMinObjective});
% 使用最佳参数训练最终模型
best_params = results.XAtMinObjective;
optimized_forecaster = train_final_model(X_train, y_train, best_params);
fprintf('超参数优化完成\n');
end
function loss = train_evaluate_model(X_train, y_train, params)
% 训练并评估模型
cv = cvpartition(length(y_train), 'KFold', 5);
losses = zeros(cv.NumTestSets, 1);
for i = 1:cv.NumTestSets
train_idx = training(cv, i);
test_idx = test(cv, i);
X_tr = X_train(train_idx, :);
y_tr = y_train(train_idx);
X_te = X_train(test_idx, :);
y_te = y_train(test_idx);
% 训练模型
model = TreeBagger(params.NumTrees, X_tr, y_tr, ...
'Method', 'regression', ...
'MinLeafSize', params.MinLeafSize);
% 预测和评估
y_pred = predict(model, X_te);
losses(i) = sqrt(mean((y_te - y_pred).^2)); % RMSE
end
loss = mean(losses);
end
参考代码 电力系统短期负荷预测 www.youwenfan.com.com/contentcnj/59804.html
电力系统短期负荷预测方案提供了：
- 数据预处理：缺失值处理、异常值检测、特征工程
- 多种预测模型：ARIMA、SVM、随机森林、LSTM等
- 模型评估：多种性能指标和可视化
- 集成学习：模型融合提高预测精度
- 超参数优化：自动寻找最佳模型参数
浙公网安备 33010602011771号