compete/app/common/limited_power_solar.py

import pandas as pd
import os
import argparse
import numpy as np
np.random.seed(42)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
current_path = os.path.dirname(__file__)
parent_path = os.path.dirname(current_path)

class LimitPower(object):
    def __init__(self, logger, args, weather_power):
        self.logger = logger
        self.args = args
        self.opt = argparse.Namespace(**args)
        self.weather_power = weather_power

    def segment_statis(self):
        """
        对总辐射-实际功率进行分段处理，获取分度的中位点，四分位间距和斜率
        :return: glob_rp 总辐射分段
        """
        segs = [x for x in range(50, 2000, 100)]    # 对辐照度以100为间隔进行分段
        xs = [segs[i-1]+x if i>0 else 25 for i, x in enumerate([50 for _ in segs])]  # 分段的中间点
        glob_rp = {}       # dict: key 辐照度分段中间点 value 分段内的实际功率
        for index, row in self.weather_power.iterrows():
            glob_ = row[self.opt.usable_power_s["env"]]
            rp = row[self.opt.target]
            for i, seg in enumerate(segs):
                if glob_ <= seg and not (i > 0 and rp < 1):
                    glob_rp.setdefault(xs[i], []).append(rp)
                    break
        for i, x in enumerate(xs):
            rps = glob_rp.get(x)
            if rps is None:
                glob_rp = {k: v for k, v in glob_rp.items() if k not in xs[xs.index(x):]}
                break
            x_l = xs[i-1] if i > 0 else 0
            q2_l = glob_rp[xs[i-1]][0] if i > 0 else 0
            q1 = np.percentile(rps, self.opt.usable_power_s['down_fractile'])     # 实际功率下四分位点
            q2 = np.percentile(rps, 50)  # 实际功率中位点
            q3 = np.percentile(rps, self.opt.usable_power_s['up_fractile'])     # 实际功率上四分位点
            iqr = q3 -q1    # 四分位间距
            k1 = round(q2/x, 5)  # 整体斜率
            k2 = round((q2-q2_l)/(x-x_l), 5)    # 趋势斜率，相对上一个中位点
            glob_rp[x] = [q2, iqr, k1, k2]   # 更新dict
        return glob_rp

    def mapping_relation(self, glob_rp):
        """
        拟合分段处理后的斜率和偏移量
        :param glob_rp: 总辐射分段
        :return: k_final 斜率 bias 实际功率的分布宽度， glob_rp 总辐射分段
        """
        ks, iqrs, delete_x, tag_x = [], [], [], []   # ks所有分段斜率集合，iqrs所有分段间距集合,delete_x删除的x坐标集合
        for x, values in glob_rp.items():
            k1 = values[-2]
            k2 = values[-1]
            iqrs.append(values[-3])
            if k1 > 0 and k2 > 0:   # 清除趋势小于等于0的斜率
                ks.append(k1)
                tag_x.append(x)
            else:
                delete_x.append(x)
                # print("删除的斜率：", k1, k2)
        bias = round(np.median(iqrs), 3)  # 中位点
        # print("++++1", ks)
        mean = np.mean(ks)  # 均值
        std = np.std(ks)    # 标准差
        ks = np.array(ks)
        z_score = (ks-mean)/std # z均值
        # print("----", z_score)
        outliers = np.abs(z_score) > self.opt.usable_power_s['outliers_threshold']    # 超过阈值为离群点
        ks = ks[~outliers]  # 消除离群点
        delete_x1 = list(np.array(tag_x)[outliers]) # 清除大于阈值的离群点
        k_final = round(np.mean(ks), 5)  # 对清洗后的斜率做平均
        # print("++++2：", ks)
        delete_x.extend(delete_x1)
        self.logger.info("拟合可用功率，删除的斜率：" + ' '.join([str(x) for x in delete_x]))
        glob_rp = {k: v for k, v in glob_rp.items() if k not in delete_x}   # 清洗后剩下的分段点位
        return k_final, bias, glob_rp

    def filter_unlimited_power(self, zfs, real_power, k, b):
        """
        预测可用功主方法
        :param zfs: 要预测可用功率的总辐射
        :param k: 斜率
        :param b: 偏移量
        :return: 预测的可用功率
        """
        high = k*zfs+b/2 if k*zfs+b/2 < self.opt.cap else self.opt.cap
        low = k*zfs-b/2 if k*zfs-b/2 > 0 else 0
        if low <= real_power <= high:
            return True
        else:
            return False

    def clean_limited_power(self, name, is_repair=False):
        if is_repair is True:
            glob_rp = self.segment_statis()
            k_final, bias, glob_rp = self.mapping_relation(glob_rp)
            self.opt.usable_power_s['k'] = float(k_final)
            self.opt.usable_power_s['bias'] = float(bias)
        new_weather_power = []
        for index, row in self.weather_power.iterrows():
            zfs = row[self.opt.usable_power_s["env"]]
            rp = row[self.opt.target]
            if self.filter_unlimited_power(zfs, rp, self.opt.usable_power_s['k'], self.opt.usable_power_s['bias'] * self.opt.usable_power_s['coe']):
                row['c'] = 'red'
                new_weather_power.append(row)
            else:
                row['c'] = 'blue'
                new_weather_power.append(row)
        new_weather_power = pd.concat(new_weather_power, axis=1).T
        new_weather_power.plot.scatter(x=self.opt.usable_power_s["env"], y='Power', c='c')
        plt.savefig(parent_path + '/figs/测光法{}.png'.format(name))
        new_weather_power = new_weather_power[new_weather_power['c'] == 'red']
        number = len(new_weather_power)
        self.logger.info("测光法-未清洗限电前，总共有：{}条数据".format(len(self.weather_power)))
        self.logger.info("测光法-清除限电后保留的点有：" + str(number) + " 占比：" + str(round(number / len(self.weather_power), 2)))
        return new_weather_power.loc[:, [self.opt.col_time, self.opt.target]]



if __name__ == '__main__':
    power = pd.read_csv('2023-12-01至2023-12-23实际功率导出文件.csv', date_parser=['时间'])
    weather = pd.read_csv('2023-12-01至2023-12-23气象站数据导出文件.csv', date_parser=['时间'])
    weather_power = pd.merge(weather, power, on='时间')  # 联立数据
    # glob_rp = segment_statis(weather_power)
    # k_final, bias, glob_rp = mapping_relation(glob_rp)