function [pv_intervals, load_fuzzy] = uncertainty_modeling(file_path)
% 读取Excel数据
data = readtable(file_path);
% 提取光伏出力数据(假设Active_Power列存在)
if ismember('Active_Power', data.Properties.VariableNames)
pv_output = data.Active_Power;
else
error('Active_Power column not found in the data.');
end
% 过滤无效数据(值为0或NaN)
valid_idx = pv_output > 0 & ~isnan(pv_output);
pv_output = pv_output(valid_idx);
% 步骤1:光伏出力区间模型(基于论文公式1-5到1-8)
% 计算光伏出力效率因子(归一化处理)
max_output = max(pv_output);
efficiency_factors = pv_output / max_output;
% 按时刻分组(假设数据按时间顺序排列)
hours = floor((0:length(pv_output)-1)' * 5 / 60); % 5分钟间隔数据
unique_hours = unique(hours);
% 初始化存储结构
pv_intervals = struct();
pv_intervals.upper = zeros(size(unique_hours));
pv_intervals.lower = zeros(size(unique_hours));
% 计算每个时刻的上下界
for i = 1:length(unique_hours)
t = unique_hours(i);
idx = hours == t;
t_factors = efficiency_factors(idx);
% 计算平均值(公式1-6)
avg_factor = mean(t_factors);
% 分组(公式1-7)
high_group = t_factors(t_factors > avg_factor);
low_group = t_factors(t_factors < avg_factor);
% 计算上下界(公式1-8)
if ~isempty(high_group)
pv_intervals.upper(i) = mean(high_group);
else
pv_intervals.upper(i) = avg_factor;
end
if ~isempty(low_group)
pv_intervals.lower(i) = mean(low_group);
else
pv_intervals.lower(i) = avg_factor;
end
end
% 步骤2:负荷模糊模型(基于论文公式1-10和1-11)
% 计算负荷波动因子(使用辐射数据作为代理)
if ismember('Radiation_Global_Tilted', data.Properties.VariableNames)
radiation = data.Radiation_Global_Tilted(valid_idx);
max_radiation = max(radiation);
beta_t = radiation / max_radiation;
else
% 若无辐射数据,使用光伏出力模式作为负荷代理
beta_t = efficiency_factors;
warning('Using PV output pattern as load proxy. Radiation data not found.');
end
% 建立三角模糊模型(公式1-11)
lambda = 0.05; % 模糊参数,根据论文设置
load_fuzzy = struct();
load_fuzzy.lower = (1 - lambda) * beta_t;
load_fuzzy.nominal = beta_t;
load_fuzzy.upper = (1 + lambda) * beta_t;
% 可视化结果
visualize_results(unique_hours, pv_intervals, beta_t, load_fuzzy);
end
function visualize_results(hours, pv_intervals, beta_t, load_fuzzy)
% 光伏出力区间可视化
figure('Position', [100, 100, 1200, 800]);
subplot(2, 1, 1);
plot(hours, pv_intervals.lower, 'b', 'LineWidth', 1.5);
hold on;
plot(hours, pv_intervals.upper, 'r', 'LineWidth', 1.5);
fill([hours; flipud(hours)], [pv_intervals.lower; flipud(pv_intervals.upper)], ...
[0.8 0.8 1], 'FaceAlpha', 0.3, 'EdgeColor', 'none');
title('PV Output Efficiency Factor Intervals', 'FontSize', 14);
xlabel('Hour of Day', 'FontSize', 12);
ylabel('Efficiency Factor', 'FontSize', 12);
legend('Lower Bound', 'Upper Bound', 'Uncertainty Interval', 'Location', 'best');
grid on;
xlim([min(hours), max(hours)]);
% 负荷模糊模型可视化
subplot(2, 1, 2);
plot(1:length(beta_t), load_fuzzy.lower, 'g--', 'LineWidth', 1.5);
hold on;
plot(1:length(beta_t), load_fuzzy.nominal, 'b-', 'LineWidth', 2);
plot(1:length(beta_t), load_fuzzy.upper, 'm--', 'LineWidth', 1.5);
fill([1:length(beta_t), fliplr(1:length(beta_t))], ...
[load_fuzzy.lower; flipud(load_fuzzy.upper)]', ...
[0.8 1 0.8], 'FaceAlpha', 0.3, 'EdgeColor', 'none');
title('Load Fuzzy Model', 'FontSize', 14);
xlabel('Time Index', 'FontSize', 12);
ylabel('Normalized Load', 'FontSize', 12);
legend('Lower Bound (1-λ)', 'Nominal Load', 'Upper Bound (1+λ)', 'Uncertainty Region', ...
'Location', 'best');
grid on;
xlim([1, length(beta_t)]);
% 保存结果
save('uncertainty_models.mat', 'pv_intervals', 'load_fuzzy');
disp('Uncertainty models saved to uncertainty_models.mat');
end帮我检查一下是否读取到text这个excel文件中的数据
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