main.ipynb

{
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this again after editing submodules so Colab uses the updated versions\n",
    "from citylearn import  CityLearn\n",
    "from pathlib import Path\n",
    "from agent import Agent\n",
    "import numpy as np                                                                                                                                                                                      \n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment\n",
    "climate_zone = 5\n",
    "params = {'data_path':Path(\"data/Climate_Zone_\"+str(climate_zone)), \n",
    "        'building_attributes':'building_attributes.json', \n",
    "        'weather_file':'weather_data.csv', \n",
    "        'solar_profile':'solar_generation_1kW.csv', \n",
    "        'carbon_intensity':'carbon_intensity.csv',\n",
    "        'building_ids':[\"Building_\"+str(i) for i in [1,2,3,4,5,6,7,8,9]],\n",
    "        'buildings_states_actions':'buildings_state_action_space.json', \n",
    "        'simulation_period': (0, 8760*4-1), \n",
    "        'cost_function': ['ramping','1-load_factor','average_daily_peak','peak_demand','net_electricity_consumption','carbon_emissions'], \n",
    "        'central_agent': False,\n",
    "        'save_memory': False }\n",
    "\n",
    "# Contain the lower and upper bounds of the states and actions, to be provided to the agent to normalize the variables between 0 and 1.\n",
    "env = CityLearn(**params)\n",
    "observations_spaces, actions_spaces = env.get_state_action_spaces()\n",
    "\n",
    "# Provides information on Building type, Climate Zone, Annual DHW demand, Annual Cooling Demand, Annual Electricity Demand, Solar Capacity, and correllations among buildings\n",
    "building_info = env.get_building_information()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device:cuda\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'ramping': 1.6146312,\n",
       "  '1-load_factor': 1.1158112003254197,\n",
       "  'average_daily_peak': 1.1334853,\n",
       "  'peak_demand': 1.1599934,\n",
       "  'net_electricity_consumption': 1.0157715,\n",
       "  'carbon_emissions': 1.0229925,\n",
       "  'total': 1.177114183933326,\n",
       "  'coordination_score': 1.2559802759152476},\n",
       " {'ramping_last_yr': 1.1639097,\n",
       "  '1-load_factor_last_yr': 1.0558413421324109,\n",
       "  'average_daily_peak_last_yr': 1.0668069,\n",
       "  'peak_demand_last_yr': 1.1332848,\n",
       "  'net_electricity_consumption_last_yr': 1.0071987,\n",
       "  'carbon_emissions_last_yr': 1.0151038,\n",
       "  'coordination_score_last_yr': 1.1049606838626467,\n",
       "  'total_last_yr': 1.0781579896667743})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_agent = {'building_ids':[\"Building_\"+str(i) for i in [1,2,3,4,5,6,7,8,9]],\n",
    "                 'buildings_states_actions':'buildings_state_action_space.json', \n",
    "                 'building_info':building_info,\n",
    "                 'observation_spaces':observations_spaces, \n",
    "                 'action_spaces':actions_spaces}\n",
    "\n",
    "# Instantiating the control agent(s)\n",
    "agents = Agent(**params_agent)\n",
    "\n",
    "state = env.reset()\n",
    "done = False\n",
    "\n",
    "action, coordination_vars = agents.select_action(state)    \n",
    "while not done:\n",
    "    next_state, reward, done, _ = env.step(action)\n",
    "    action_next, coordination_vars_next = agents.select_action(next_state)\n",
    "    agents.add_to_buffer(state, action, reward, next_state, done, coordination_vars, coordination_vars_next)\n",
    "    coordination_vars = coordination_vars_next\n",
    "    state = next_state\n",
    "    action = action_next\n",
    "\n",
    "env.cost()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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