diff --git a/peakshaving_analyzer/input.py b/peakshaving_analyzer/input.py
index eb47184..c437805 100644
--- a/peakshaving_analyzer/input.py
+++ b/peakshaving_analyzer/input.py
@@ -7,9 +7,12 @@
 
 import pandas as pd
 import pgeocode
+import plotly.express as px
+import plotly.graph_objects as go
 import requests
 import sqlalchemy
 import yaml
+from statsmodels.tsa import seasonal
 
 from peakshaving_analyzer.common import IOHandler
 
@@ -78,10 +81,72 @@ def timeseries_to_df(self):
 
         df["consumption_kw"] = self.consumption_timeseries
         df["energy_price_eur"] = self.price_timeseries["grid"]
-        df["new_pv_generation_kw"] = self.new_pv_generation_timeseries["consumption_site"]
+
+        if self.new_pv_generation_timeseries is not None:
+            df["new_pv_generation_kw"] = self.new_pv_generation_timeseries["consumption_site"]
 
         return df
 
+    def plot_load_duration_curve(self):
+        ts_df = self.timeseries_to_df()
+
+        fig = px.line(
+            data_frame=ts_df.sort_values("consumption_kw", ascending=False, ignore_index=True),
+            x=ts_df.index,
+            y="consumption_kw",
+            title="Load duration curve",
+        )
+        fig.update_layout(xaxis_title="Number of times", yaxis_title="Load in kW")
+
+        fig.show()
+
+    def plot_load_histogram(self):
+        ts_df = self.timeseries_to_df()
+
+        fig = px.histogram(data_frame=ts_df, x="consumption_kw", title="Histogram of load")
+        fig.update_layout(xaxis_title="Load in kW")
+
+        fig.show()
+
+    def plot_load_box(self):
+        ts_df = self.timeseries_to_df()
+
+        fig = px.box(
+            data_frame=ts_df,
+            x="consumption_kw",
+            title="Boxplot of load",
+        )
+        fig.update_layout(xaxis_title="Load in kW")
+
+        fig.show()
+
+    def seasonal_decompose(self) -> seasonal.DecomposeResult:
+        ts_df = self.timeseries_to_df()
+        ts_df.index = self.timestamps.copy()
+
+        decompose_result = seasonal.seasonal_decompose(x=ts_df["consumption_kw"])
+
+        return decompose_result
+
+    def plot_seasonal_decompose(self):
+        decompose_result = self.seasonal_decompose()
+
+        fig = go.Figure()
+        for var in ["seasonal", "trend", "resid"]:
+            fig.add_scatter(
+                x=self.timestamps,
+                y=getattr(decompose_result, var),
+                name=var,
+            )
+
+        fig.update_layout(
+            title="Seasonal decomposition",
+            xaxis_title="Time",
+            yaxis_title="Load in kW",
+        )
+
+        fig.show()
+
 
 def load_yaml_config(config_file_path: Path | str, test_mode: bool = False) -> Config:
     config_path = Path(config_file_path)