diff --git a/pyFTS/models/multivariate/common.py b/pyFTS/models/multivariate/common.py
index e9c4f3b..97b942b 100644
--- a/pyFTS/models/multivariate/common.py
+++ b/pyFTS/models/multivariate/common.py
@@ -54,7 +54,7 @@ def fuzzyfy_instance_clustered(data_point, cluster, **kwargs):
     alpha_cut = kwargs.get('alpha_cut', 0.0)
     mode = kwargs.get('mode', 'sets')
     fsets = []
-    for fset in cluster.search(data_point):
+    for fset in cluster.search(data_point, type='name'):
         if cluster.sets[fset].membership(data_point) > alpha_cut:
             if mode == 'sets':
                 fsets.append(fset)
diff --git a/pyFTS/models/multivariate/grid.py b/pyFTS/models/multivariate/grid.py
index 6467e09..0561c6c 100644
--- a/pyFTS/models/multivariate/grid.py
+++ b/pyFTS/models/multivariate/grid.py
@@ -48,13 +48,9 @@ class IncrementalGridCluster(partitioner.MultivariatePartitioner):
             return ret
 
         if self.kdtree is not None:
-            fsets = self.search(data, **kwargs)
+            fsets = self.search(data, type='name')
         else:
-            fsets = self.incremental_search(data, **kwargs)
-
-        if len(fsets) == 0:
-            fsets = self.incremental_search(data, **kwargs)
-            raise Exception("{}".format(data))
+            fsets = self.incremental_search(data, type='name')
 
         mode = kwargs.get('mode', 'sets')
         if mode == 'sets':
diff --git a/pyFTS/tests/multivariate.py b/pyFTS/tests/multivariate.py
index fd562b1..bb3010d 100644
--- a/pyFTS/tests/multivariate.py
+++ b/pyFTS/tests/multivariate.py
@@ -188,7 +188,7 @@ vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
 
 from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
 
-fs = grid.IncrementalGridCluster(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
+fs = grid.GridCluster(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
 
 
 model = cmvfts.ClusteredMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg,
@@ -196,4 +196,4 @@ model = cmvfts.ClusteredMVFTS(explanatory_variables=[vmonth, vhour, vavg], targe
 
 model.fit(train)
 
-print(len(model))
+model.predict(test)