diff --git a/pywatts/db.py b/pywatts/db.py
index 88af8f7..b877125 100644
--- a/pywatts/db.py
+++ b/pywatts/db.py
@@ -34,21 +34,14 @@ class Result(Model):
 
 
 def rows_to_df(indices):
-    temps = []
     dcs = []
-    winds = []
 
     db.connect()
 
     for result in Result.select().where(Result.id << indices):
-        temps += result.temperature
         dcs += result.dc_output
-        winds += result.wind_speed
 
     db.close()
 
     return pd.DataFrame(
-        {'temp': temps,
-         'dc': dcs,
-         'wind': winds
-         })
+        {'dc': dcs})
diff --git a/pywatts/eval_training.py b/pywatts/eval_training.py
index ab95090..439b3bf 100644
--- a/pywatts/eval_training.py
+++ b/pywatts/eval_training.py
@@ -4,13 +4,13 @@ from pywatts.routines import *
 from pywatts import kcross
 
 NUM_STATIONS_FROM_DB = 75
-K = 2
+K = 10
 NUM_EVAL_STATIONS = 40
 TRAIN = True
 PLOT = True
-TRAIN_STEPS = 1
-TOTAL_STEPS = 2
-NUM_QUERIES = 1
+TRAIN_STEPS = 10
+TOTAL_STEPS = 6
+NUM_QUERIES = 5
 PREDICT_QUERY = "query-sample_24hour.json"
 PREDICT_RESULT = PREDICT_QUERY.replace("query", "result")
 FIGURE_OUTPUT_DIR = "../figures/"
diff --git a/pywatts/neural.py b/pywatts/neural.py
index 1304b8f..e6e42b3 100644
--- a/pywatts/neural.py
+++ b/pywatts/neural.py
@@ -30,11 +30,11 @@ def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=1):
 
 class Net:
     __regressor = None
-    __feature_cols = [tf.feature_column.numeric_column(col) for col in ['dc', 'temp', 'wind']]
+    __feature_cols = [tf.feature_column.numeric_column(col) for col in ['dc']]
 
     def __init__(self, feature_cols=__feature_cols):
         self.__regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
-                                                     hidden_units=[128, 512, 128],
+                                                     hidden_units=[64, 128, 64],
                                                      model_dir='tf_pywatts_model')
 
     def train(self, training_data, training_results, batch_size, steps):
diff --git a/pywatts/photovoltaic_gruppe1.py b/pywatts/photovoltaic_gruppe4.py
similarity index 90%
rename from pywatts/photovoltaic_gruppe1.py
rename to pywatts/photovoltaic_gruppe4.py
index 3889f6c..2378d7c 100644
--- a/pywatts/photovoltaic_gruppe1.py
+++ b/pywatts/photovoltaic_gruppe4.py
@@ -10,7 +10,7 @@ from pywatts.routines import *
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 if len(sys.argv) != 2:
-    print("Usage: python photovoltaic_gruppe1.py <file.json>")
+    print("Usage: python photovoltaic_gruppe4.py <file.json>")
     exit(1)
 
 json_file = sys.argv[1]  # json file
diff --git a/pywatts/routines.py b/pywatts/routines.py
index 54e3beb..e500cd7 100644
--- a/pywatts/routines.py
+++ b/pywatts/routines.py
@@ -9,7 +9,7 @@ from random import randint
 
 def train_split(data, size):
     used_idxs = []
-    X_values = {'dc': [], 'temp': [], 'wind': []}
+    X_values = {'dc': []}
     y_values = []
     for i in range(size):
         rnd_idx = randint(0, data.size / data.shape[1] - 337)
@@ -20,8 +20,6 @@ def train_split(data, size):
             used_idxs.append(rnd_idx)
 
         X_values['dc'].extend(data['dc'][rnd_idx:rnd_idx + 336].tolist())
-        X_values['temp'].extend(data['temp'][rnd_idx:rnd_idx + 336].tolist())
-        X_values['wind'].extend(data['wind'][rnd_idx:rnd_idx + 336].tolist())
         y_values.append(data['dc'][rnd_idx + 337].tolist())
 
     return pandas.DataFrame.from_dict(X_values), pandas.DataFrame.from_dict({'dc': y_values})
@@ -31,9 +29,7 @@ def input_query(json_str, idx=0):
     tmp_df = pandas.read_json(json_str)
 
     return pandas.DataFrame.from_dict(
-        {'dc': tmp_df['dc'][idx],
-         'temp': tmp_df['temp'][idx],
-         'wind': tmp_df['wind'][idx]}
+        {'dc': tmp_df['dc'][idx]}
     )
 
 def input_queries(json_str):
@@ -48,9 +44,7 @@ def input_queries(json_str):
     queries = []
     for i in range(len(tmp_df)):
         queries.append(pandas.DataFrame.from_dict(
-            {'dc': tmp_df['dc'][i],
-             'temp': tmp_df['temp'][i],
-             'wind': tmp_df['wind'][i]}
+            {'dc': tmp_df['dc'][i]}
         ))
     return oneH, queries
 
@@ -85,7 +79,7 @@ def plot_training(evaluation):
 def predict(nn, X_pred):
     pred = nn.predict1h(X_pred)
     # Cap results to 0
-    predictions = np.array([max(p['predictions'], 0) for p in pred])
+    predictions = np.array([max(p['predictions'], [0]) for p in pred])
     return predictions