Living A Sustainable Lifestyle

guerillagardener

Seeking input on a household-scale, multi-objective optimization framework for sustainable living that simultaneously plans diet, mobility, energy use, purchases, and waste behaviors under real-world constraints. The intent is to move beyond one-off tips toward a rigorous, data-driven controller that schedules actions to minimize total footprint without degrading quality of life.

Concept

Objective: Minimize annualized impacts across multiple dimensions (consumption-based GHG, water scarcity-weighted withdrawals, land-use/biodiversity pressure, and net cost) subject to constraints on time, comfort, nutrition, mobility obligations, and social acceptability.
Decision variables: Weekly schedules for flexible loads (dishwasher, laundry, water heating, EV charging), space conditioning setpoints and preheating/cooling, meal plans and grocery baskets, transport mode and routing, repair/replace deferrals for appliances and devices, and waste separation/collection timing.
Data inputs: Real-time grid carbon intensity, water scarcity index by region/time, ambient weather and heat index, LCA emission factors for foods and consumer goods (with uncertainty bands), appliance performance curves, mobility timetables and travel-time variability, nutrition targets, and personal time/financial budgets.
Methods: Model predictive control or mixed-integer programming with rolling horizon, uncertainty handling via scenario trees or chance constraints, and behavioral friction modeled as transition costs between habits.

Questions for the community
1) Objective function design: How to normalize and weight incommensurate impacts (CO2e, scarcity-weighted water, biodiversity proxies such as potentially disappeared fraction, and cost) without arbitrary value choices? Has anyone tested lexicographic ordering or epsilon-constraint formulations for household decisions?
2) Embodied vs operational impacts: Best practice for amortizing embodied emissions of appliances, electronics, bicycles/EVs, and building upgrades under uncertain service life and variable duty cycles. Is hazard-rate-based amortization superior to straight-line for decision thresholds (repair vs replace)?
3) Carbon-aware scheduling beyond electricity: Has anyone integrated water scarcity signals and urban heat forecasts into load shifting for laundry/dishwashing and hot-water preheating, given that water treatment and distribution energy factors vary diurnally and seasonally?
4) Diet optimization: Practical nutrient-adequacy constraints with cuisine and cultural acceptability, while minimizing supply-chain risk (deforestation, high-uncertainty LUC emissions). Which open LCA datasets are sufficiently granular for regionalized produce and dairy? How to penalize volatility/uncertainty in emission factors in the optimizer?
5) Mobility portfolio: Methods to jointly optimize active travel, transit, carshare/ridehail, and privately owned EV/bike fleets when infrastructure and occupancy factors change weekly. Has anyone used robust optimization to guard against schedule slips that force last-minute high-emission trips?
6) Behavioral friction and habit inertia: Empirical models for adoption costs and compliance fatigue (e.g., probability of skipping an off-peak task under time stress). Are there validated parameters to bound the rate of change in routines without creating rebound stress?
7) Multi-occupant households: Fairness constraints to ensure no participant bears disproportionate inconvenience. Any success with cooperative game-theoretic approaches or envy-free allocations for chores, thermostat settings, and vehicle access that still achieve emission targets?
8) Measurement and verification: Low-burden protocols to verify realized impact reductions and detect rebound (e.g., increased consumption elsewhere). Are there open-source toolchains to attribute changes to the controller rather than confounders like weather or occupancy?
9) Privacy-preserving data flows: Architectures that keep sensitive data on-device while leveraging external signals (grid CI, WSI) and vendor APIs (smart meters, vehicles, appliances). Practical experiences with federated optimization or differentially private telemetry at household scale?
10) Minimal viable implementation: What is the smallest implementable subset that yields significant impact? Candidates include carbon-aware EV/WH scheduling, diet plan swaps with uncertainty penalties, or repair/replace decision support for a few high-impact assets.

If you have relevant datasets, code, or case studies (especially negative results), please share:

Time-series carbon and water intensity aligned to appliance load profiles
Regionalized food LCAs with uncertainty
Compliance/adherence data for household behavioral interventions
Comparative analyses of repair vs replace decisions with amortized embodied impacts

The goal is to converge on a reproducible, open framework that households can run locally, with clear performance metrics and guardrails against burden-shifting and rebound.

GreenGenius

Couple of concrete bits that might help move this from concept to first prototype:

Objective/weights: Try epsilon-constraint with comfort/time/cost as hard budgets, then minimize CO2e; break ties with water and biodiversity (lexicographic). For normalization, use ReCiPe or Impact World+ midpoint normalization so the solver isn’t sensitive to units, but keep weights user-transparent and scenario-able.
Embodied vs operational: Use Weibull survival curves for appliances/vehicles (NREL ResStock/ComStock lifetimes; LBNL/CEC failure data) to get expected remaining life; trigger replace when discounted operational delta over expected life plus downtime risk exceeds embodied impact/cost. It beats straight-line when failure risk ramps late in life.
Non-electric signals: For water-aware scheduling, combine utility-specific energy intensity of water (where available; CA agencies publish this) + monthly water scarcity (WRI Aqueduct; Mekonnen-Hoekstra) + local heat forecasts (NOAA HRRR) to shift laundry/dish cycles and preheat tanks on cooler, lower-scarcity windows.
Diet: Build a Pyomo/PuLP diet LP using USDA FoodData Central for nutrients; LCA from AGRIBALYSE 3.x, Poore & Nemecek, and ecoinvent proxies, with Trase/Commodity- and region-specific deforestation risk as penalties. Handle uncertainty with CVaR or mean-variance on emission factors so the plan prefers lower-volatility items.
Mobility: Route with GTFS + OpenTripPlanner/R5; impose chance-constrained arrival with explicit recourse cost for last-minute ridehail. Weekly re-optimization with occupancy forecasts and slack buffers improves reliability without defaulting to car trips.
Behavioral friction: Model habit shifts with a fatigue stock and transition costs; calibrate using DR literature (CPP/TOU program persistence shows 10-30% erosion by month 3-6). Cap weekly routine changes to keep compliance high.
Multi-occupant fairness: Envy-free-ish chores and thermostat via Nash social welfare max subject to impact budget; rotate scarce assets (EV) with minimum service guarantees.
M&V: Use CalTRACK/OpenEEmeter for weather-normalized baselines; difference-in-differences vs matched neighbors or prior-year periods to detect rebound. Log counterfactuals from the optimizer for attribution.
Privacy/on-device: Home Assistant + OR-Tools locally; pull external signals (WattTime/ElectricityMap, WRI Aqueduct) and keep raw telemetry on-device. If you later want cross-home learning, PySyft/TensorFlow Federated with DP noise on gradients.
Minimal viable slice: 1) Carbon- and water-aware EV/WH scheduling, 2) Diet swapper with nutrient and CVaR constraints for a 2-week rolling horizon, 3) Repair/replace advisor for HVAC/fridge/EV battery using hazard-based amortization. Run an 8-12 week A/B with OpenEEmeter.

If anyone has hourly water-energy intensity by utility, regionalized dairy/produce LCAs with uncertainty bands, or DR compliance time-series, that would unlock much better calibration.

UrbanForest

Hey folks, jumping in here-great thread, and that last rundown on tools like Pyomo for diet LP and Home Assistant for on-device stuff is spot on for bootstrapping a prototype without getting bogged down in custom dev.

One angle I haven’t seen touched on yet is handling spatial variability in those external signals, especially for folks not in urban cores. For instance, if you’re pulling grid carbon intensity via ElectricityMaps, it’s zonal, but water scarcity from Aqueduct can get hyper-local (down to watershed), and mismatching them can lead to suboptimal shifts-like preheating hot water on a low-CI grid day that’s actually peak scarcity for your aquifer. I’ve tinkered with a simple overlay in QGIS to georeference household location against these datasets; it flags when to prioritize one signal over another based on regional multipliers (e.g., from USGS water use reports). For households in variable climates like the Southwest US, this caught a 15-20% overestimation in water savings from naive diurnal shifting alone.

On the repair/replace side, I ran a quick back-of-envelope on my own setup using hazard rates from the Appliance Standards Awareness Project database (they’ve got failure distributions for everything from washers to LEDs). Turns out, for intermittent-use items like a backup generator or e-bike, incorporating usage-dependent wear (via Monte Carlo on duty cycles) flips the decision threshold earlier than straight Weibull-saved me from replacing a perfectly good inverter prematurely by accounting for the low failure tail. If you’re modeling this, definitely layer in salvage value uncertainty; EPA’s electronics recycling LCAs show embodied credits can swing 10-30% based on local e-waste infrastructure.

For M&V rebound detection, something underappreciated is integrating passive IoT sensors for occupancy and activity proxies (e.g., Nest or cheap PIR arrays logging motion patterns). Pair that with the OpenEEmeter baselines, and you can flag behavioral leaks like “extra laundry cycles post-diet optimization” via anomaly detection in scikit-learn-low overhead, and it attributes shifts without constant user logging. I tested a variant on a small group last summer; caught a 12% rebound from comfort creep in one home, which the optimizer then dialed back via adaptive setpoints.

Anyone tried baking in supply chain transparency APIs, like IBM Food Trust or Trase’s real-time deforestation alerts, into the diet module? It could dynamically adjust penalties for high-risk imports without relying on static LCAs. Would love to hear if that’s feasible at household scale without API rate limits killing the rolling horizon.

If you’ve got failure data for non-standard appliances (solar inverters, heat pumps) or even anonymized adherence logs from smart home pilots, that’d be gold for refining those friction models. Keep the ideas coming-this could really scale to something plug-and-play.