From c58b1257282730b7ea16e73e169f72dd03abcc2e Mon Sep 17 00:00:00 2001
From: andrewlyden <36414677+andrewlyden@users.noreply.github.com>
Date: Mon, 17 Jun 2024 11:21:33 +0100
Subject: [PATCH] Update README.md

---
 README.md | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 66ca568..7fac9c0 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ This tool was developed as part of a PhD, "Modelling and Design of Local Energy
 git clone git@github.com:andrewlyden/PyLESA.git
 ```
 
-3. Install the `PyLESA` python virtual environment:
+3. Install the `PyLESA` python virtual environment. If using Linux:
 ```
 python3.10 -m venv venv
 source venv/bin/activate
@@ -24,6 +24,14 @@ python -m pip install --upgrade pip
 python -m pip install -r requirements.txt
 ```
 
+If using Windows and Powershell:
+```
+python -m venv venv
+.\venv\Scripts\activate.ps1
+python -m pip install --upgrade pip
+python -m pip install -r requirements.txt
+```
+
 4. Define and gather data on the local energy system to be modelled including resources, demands, supply, storage, grid connection, and control strategy. Define the increments and ranges to be modelled within the required parametric design. Input all this data using one of the template Excel Workbooks from the [inputs](./inputs) folder.
 
 5. Optionally run the demand ([heat_demand.py](./pylesa/demand/heat_demand.py) and [electricity_demand.py](./pylesa/demand/electricity_demand.py)) and resource assessment methods (see PhD thesis for details) to generate hourly profiles depending on available data. Input generated profiles into the Excel Workbook.