光伏发电量预测机器学习数据集

import tensorflow as tf import pandas as pd import numpy as np # 基于深度学习DNN LSTM网络的光伏发电量预测 # 取训练数据 df=pd.read_csv(r"public.train.csv",header=None) datatrain=np.array(df) #提取特征值，形成输入数据 #dataxs=datatrain[1:,[0,1,2,3,4,8,9,10,11,12,13,17,18,19]]#9000x8 dataxs=datatrain[1:,:20] dataxshlen=len(dataxs)#训练输入数据的行数 dataxsllen=len(dataxs[0])#训练输入数据的列数 for i in range(dataxshlen): for j in range(dataxsllen): dataxs[i][j]=float(dataxs[i][j]) #形成输出数据 datays=datatrain[1:,[20]]#9000x1 datayshlen=dataxshlen#训练输出数据的行数 dataysllen=len(datays[0])#训练输出数据的列数 for i in range(datayshlen): for j in range(dataysllen): datays[i][j]=float(datays[i][j]) print(datays[0][0]) #归一化,datays取值范围为（0.05，0.95） #datays_nor=0.05+(datays-np.min(datays))/(np.max(datays)-np.min(datays))*(0.95-0.05) print(dataxs,datays,dataxshlen,dataxsllen,datayshlen,dataysllen) #读取测试数据 df1=pd.read_csv(r"public.test.csv",header=None) datatest=np.array(df1) #提取特征值，形成输入数据 #dataxs_test=datatest[1:,[0,1,2,3,4,8,9,10,11,12,13,17,18,19]]#9000x8 dataxs_test=datatest[1:,:20] dataxs_testhlen=len(dataxs_test)#训练输入数据的行数 dataxs_testllen=len(dataxs_test[0])#训练输入数据的列数 for i in range(dataxs_testhlen): for j in range(dataxs_testllen): dataxs_test[i][j]=float(dataxs_test[i][j]) #测试比对数据 df2=pd.read_csv(r"submit_example.csv",header=None) datatest1=np.array(df2) datays_test=datatest1[0:,1:2]#9000x1 datays_testhlen=dataxs_testhlen#训练输出数据的行数 datays_testllen=len(datays_test[0])#训练输出数据的列数 for i in range(datays_testhlen): for j in range(datays_testllen): datays_test[i][j]=float(datays_test[i][j]) print(datays_test[0][0]) #归一化,datays取值范围为（0.05，0.95） #datays_nor_test=0.05+(datays_test-np.min(datays_test))/(np.max(datays_test)-np.min(datays_test))*(0.95-0.05) print(dataxs_test,datays_test,dataxs_testhlen,dataxs_testllen,datays_testhlen,datays_testllen) def add_layer(inputs,in_size,out_size,activation_function=None): Weights=tf.Variable(tf.truncated_normal([in_size,out_size],stddev=0.1)) Biases=tf.Variable(tf.constant(0.1,shape=[out_size])) xW_plus_b=tf.matmul(inputs,Weights)+Biases if activation_function is None: outputs=xW_plus_b else: outputs=activation_function(xW_plus_b) return outputs #define placeholder for inputs to network xs=tf.placeholder(tf.float32,[None,20])# ys=tf.placeholder(tf.float32,[None,1]) keep_prop=tf.placeholder(tf.float32) #layer1 datays_nor_layer1=add_layer(xs,20,10,activation_function=tf.sigmoid) #layer2 datays_nor_layer2=add_layer(datays_nor_layer1,10,20,activation_function=tf.nn.relu) #perdiction prediction=add_layer(datays_nor_layer2,20,1,activation_function=None) #the error between prediction and real data loss=tf.sqrt(tf.reduce_mean(tf.square(prediction-ys))) train_step=tf.train.GradientDescentOptimizer(0.9).minimize(loss) #train_step=tf.train.AdamOptimizer(1e-4).minimize(loss) #初始化 sess=tf.Session() sess.run(tf.global_variables_initializer()) #模型精确度 def compute_accuracy(v_xs,v_ys): global prediction y_pre=sess.run(prediction,feed_dict={xs:v_xs,keep_prop:0.5}) #y_pre_tru=(y_pre-0.05)*(np.max(datays)-np.min(datays))/(0.95-0.05) accuracy_prediction=1/(1+tf.sqrt(tf.reduce_mean(tf.square(y_pre-ys)))) #accuracy_prediction_tru=1/(1+tf.sqrt(tf.reduce_mean(tf.square(y_pre_tru-datays_test)))) result=sess.run(accuracy_prediction,feed_dict={xs:v_xs,ys:v_ys,keep_prop:0.5}) print(y_pre) return result for k in range(2000): #print(batch_xs,batch_ys,len(batch_xs)) sess.run(train_step,feed_dict={xs:dataxs,ys:datays,keep_prop:0.5}) #sess.run(train_step,feed_dict={xs:dataxs_test,ys:datays_nor_test}) if k%500==0: #print(k,sess.run(loss,feed_dict={xs:dataxs,ys:datays_nor})) print(compute_accuracy(dataxs_test,datays_test))