Alaskan Homesteading Hacks Powered by Simple AI Scripts

When you live in a cabin in Caswell Lakes, Alaska, you learn quickly that the margin between "everything is fine" and "everything is a problem" is thinner than you think.

Last winter my well pump relay failed at negative thirty-two degrees. The pipe from the well house to the cabin started losing pressure in a pattern I'd seen before but couldn't quite place. I had a temperature sensor on the well house and a pressure gauge I'd wired into a Raspberry Pi months earlier, mostly out of curiosity. The data was sitting in a CSV file. I wrote a quick script that fed the last 72 hours of readings into an OpenAI prompt and asked it to identify the pattern. It came back in under two seconds: "Pressure drop pattern is consistent with relay cycling failure, not ice blockage. Relay is likely failing to hold closed under sustained load." It was right. Saved me from tearing apart insulation looking for an ice dam that wasn't there.

That was the moment I realized that simple AI scripts aren't just useful for software projects. They're useful for keeping your house warm in Alaska.

---

The Homestead Data Problem

Here's the thing about remote homesteading that most people don't think about: you generate a surprising amount of data, and almost none of it gets analyzed.

I have temperature sensors in three locations. A weather station that logs wind speed, humidity, and barometric pressure. A solar charge controller that reports battery voltage and charge current. A propane tank gauge that I rigged to report levels over Wi-Fi. Water pressure from the well. Generator runtime hours.

All of this data lives in flat files or simple SQLite databases. Before I started writing scripts to analyze it, I'd glance at the numbers occasionally, maybe notice something was off, and react to problems after they became obvious. That's the worst possible approach to homestead maintenance. By the time a problem is obvious up here, it's usually expensive.

The shift was simple: instead of looking at raw numbers, I started feeding them to language models with specific questions.

var fs = require("fs");
var https = require("https");

function readSensorLog(filepath) {
  var raw = fs.readFileSync(filepath, "utf8");
  var lines = raw.trim().split("\n");
  var headers = lines[0].split(",");
  var rows = [];
  for (var i = 1; i < lines.length; i++) {
    var values = lines[i].split(",");
    var row = {};
    for (var j = 0; j < headers.length; j++) {
      row[headers[j].trim()] = values[j] ? values[j].trim() : "";
    }
    rows.push(row);
  }
  return rows;
}

function analyzeSensorData(data, question, callback) {
  var payload = JSON.stringify({
    model: "gpt-4o-mini",
    messages: [
      {
        role: "system",
        content: "You are an expert in off-grid homesteading systems including well pumps, solar power, propane heating, and cold weather infrastructure. Analyze sensor data and provide practical diagnostic advice. Be specific and actionable."
      },
      {
        role: "user",
        content: "Here is recent sensor data:\n\n" + JSON.stringify(data.slice(-100)) + "\n\nQuestion: " + question
      }
    ],
    max_tokens: 500
  });

  var options = {
    hostname: "api.openai.com",
    path: "/v1/chat/completions",
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": "Bearer " + process.env.OPENAI_API_KEY
    }
  };

  var req = https.request(options, function(res) {
    var body = "";
    res.on("data", function(chunk) { body += chunk; });
    res.on("end", function() {
      var result = JSON.parse(body);
      callback(null, result.choices[0].message.content);
    });
  });

  req.on("error", function(err) { callback(err); });
  req.write(payload);
  req.end();
}

var sensorData = readSensorLog("/home/pi/sensors/well_pressure.csv");
analyzeSensorData(sensorData, "Is the pressure drop pattern consistent with ice blockage or mechanical failure?", function(err, answer) {
  if (err) {
    console.error("Analysis failed:", err.message);
    return;
  }
  console.log("Analysis:", answer);
});

That's the core pattern. Read a CSV. Ask a question. Get an answer that's usually better than what I'd come up with staring at a spreadsheet at 6 AM in the dark.

---

Solar Battery Management

My solar setup is modest: four 300-watt panels, a charge controller, and a battery bank that powers essentials when the grid goes down, which happens more often than you'd expect in Mat-Su Valley. The charge controller logs voltage, current, and state of charge every five minutes.

The problem I kept running into was knowing when my batteries were actually degrading versus when they were just responding to weather patterns. A cloudy week in November looks a lot like battery degradation if you're just looking at voltage curves. But the underlying patterns are different.

I wrote a script that compares current charge-discharge cycles against a baseline I captured when the batteries were new:

var fs = require("fs");

function loadCycles(filepath) {
  var raw = fs.readFileSync(filepath, "utf8");
  var lines = raw.trim().split("\n");
  var cycles = [];
  for (var i = 1; i < lines.length; i++) {
    var parts = lines[i].split(",");
    cycles.push({
      timestamp: parts[0],
      voltage: parseFloat(parts[1]),
      current: parseFloat(parts[2]),
      soc: parseFloat(parts[3]),
      temp: parseFloat(parts[4])
    });
  }
  return cycles;
}

function compareCycles(baseline, current) {
  var baselineAvgRecovery = calculateRecoveryRate(baseline);
  var currentAvgRecovery = calculateRecoveryRate(current);

  var degradation = ((baselineAvgRecovery - currentAvgRecovery) / baselineAvgRecovery) * 100;

  return {
    baselineRecoveryRate: baselineAvgRecovery.toFixed(3),
    currentRecoveryRate: currentAvgRecovery.toFixed(3),
    degradationPercent: degradation.toFixed(1),
    needsAttention: degradation > 15
  };
}

function calculateRecoveryRate(data) {
  var recoveries = [];
  for (var i = 1; i < data.length; i++) {
    if (data[i].current > 0 && data[i - 1].current <= 0) {
      var start = i;
      while (i < data.length && data[i].current > 0) {
        i++;
      }
      if (i - start > 2) {
        var rate = (data[i - 1].soc - data[start].soc) / (i - start);
        recoveries.push(rate);
      }
    }
  }
  var sum = 0;
  for (var r = 0; r < recoveries.length; r++) {
    sum += recoveries[r];
  }
  return recoveries.length > 0 ? sum / recoveries.length : 0;
}

var baseline = loadCycles("/home/pi/solar/baseline_cycles.csv");
var recent = loadCycles("/home/pi/solar/recent_cycles.csv");
var result = compareCycles(baseline, recent);

console.log("Battery Health Report:");
console.log("  Baseline recovery rate:", result.baselineRecoveryRate, "% per interval");
console.log("  Current recovery rate:", result.currentRecoveryRate, "% per interval");
console.log("  Estimated degradation:", result.degradationPercent + "%");

if (result.needsAttention) {
  console.log("  WARNING: Battery bank may need equalization or replacement");
}

This is not sophisticated data science. It's arithmetic with a purpose. The recovery rate comparison told me my batteries were at about 12% degradation after two years, which is within spec for the AGM batteries I'm running. Without that comparison, I would have probably replaced them early out of anxiety, which would have been an $800 mistake.

---

Propane Usage Forecasting

Propane is the lifeline up here. Run out during a cold snap and you've got a serious problem. I used to check the gauge manually and do rough mental math about how many weeks I had left. That works until it doesn't.

Now I feed my propane level readings and the weather forecast into a script that estimates days remaining:

var fs = require("fs");
var https = require("https");

function getPropaneData(filepath) {
  var raw = fs.readFileSync(filepath, "utf8");
  var lines = raw.trim().split("\n");
  var readings = [];
  for (var i = 1; i < lines.length; i++) {
    var parts = lines[i].split(",");
    readings.push({
      date: parts[0],
      level: parseFloat(parts[1]),
      avgTemp: parseFloat(parts[2])
    });
  }
  return readings;
}

function estimateDaysRemaining(readings) {
  var recentReadings = readings.slice(-14);
  if (recentReadings.length < 2) return null;

  var totalDrop = recentReadings[0].level - recentReadings[recentReadings.length - 1].level;
  var days = recentReadings.length;
  var dailyRate = totalDrop / days;

  var currentLevel = recentReadings[recentReadings.length - 1].level;

  // Adjust for temperature correlation
  var avgTemp = 0;
  for (var i = 0; i < recentReadings.length; i++) {
    avgTemp += recentReadings[i].avgTemp;
  }
  avgTemp = avgTemp / recentReadings.length;

  // Colder temps increase usage roughly 2% per degree below zero F
  var tempAdjustment = 1.0;
  if (avgTemp < 0) {
    tempAdjustment = 1 + (Math.abs(avgTemp) * 0.02);
  }

  var adjustedRate = dailyRate * tempAdjustment;
  var daysLeft = adjustedRate > 0 ? Math.floor(currentLevel / adjustedRate) : 999;

  return {
    currentLevel: currentLevel.toFixed(1),
    dailyRate: dailyRate.toFixed(2),
    adjustedRate: adjustedRate.toFixed(2),
    tempAdjustment: tempAdjustment.toFixed(2),
    estimatedDaysRemaining: daysLeft,
    orderBy: new Date(Date.now() + (daysLeft - 14) * 86400000).toISOString().split("T")[0]
  };
}

var readings = getPropaneData("/home/pi/propane/levels.csv");
var estimate = estimateDaysRemaining(readings);

console.log("Propane Status:");
console.log("  Current level:", estimate.currentLevel + "%");
console.log("  Daily usage rate:", estimate.dailyRate + "% per day");
console.log("  Temp-adjusted rate:", estimate.adjustedRate + "% per day");
console.log("  Estimated days remaining:", estimate.estimatedDaysRemaining);
console.log("  Order by:", estimate.orderBy);

if (estimate.estimatedDaysRemaining < 21) {
  console.log("  ALERT: Schedule propane delivery soon");
}

The temperature adjustment is the key insight. My propane usage at ten above zero is wildly different from my usage at thirty below. The 2% per degree factor is something I calibrated over one winter of tracking actual consumption against temperature. It's not perfect, but it's close enough that my delivery scheduling has gone from "hope for the best" to "order on this date."

I've added an email notification to the alert condition so I get a message when it's time to call the propane company. Sounds trivial, but when you're deep in a project and not thinking about fuel levels, that notification is worth its weight in gold.

---

Food Storage and Freezer Monitoring

We keep a chest freezer in an unheated shed. In summer, that's fine. In winter, the ambient temperature in the shed drops well below freezing, and the freezer doesn't need to run at all. The problem is the shoulder seasons: fall and spring, when temperatures swing above and below freezing within the same day. The freezer cycles erratically, and food safety becomes a real concern if it fails during a warm spell.

I stuck a temperature sensor inside the freezer and another in the shed. The script is embarrassingly simple:

var fs = require("fs");

function checkFreezerSafety(freezerTemp, shedTemp) {
  var alerts = [];

  if (freezerTemp > 0) {
    alerts.push("CRITICAL: Freezer above 0°F (" + freezerTemp + "°F). Check compressor.");
  } else if (freezerTemp > -5) {
    alerts.push("WARNING: Freezer temperature rising (" + freezerTemp + "°F). Monitor closely.");
  }

  if (shedTemp > 40 && freezerTemp > -10) {
    alerts.push("WARNING: Shed warming up (" + shedTemp + "°F). Freezer may struggle to maintain temp.");
  }

  if (shedTemp < -20) {
    alerts.push("INFO: Shed below -20°F. Freezer compressor may not need to cycle.");
  }

  return alerts;
}

var sensorFile = "/home/pi/sensors/freezer_temps.csv";
var raw = fs.readFileSync(sensorFile, "utf8");
var lines = raw.trim().split("\n");
var latest = lines[lines.length - 1].split(",");

var freezerTemp = parseFloat(latest[1]);
var shedTemp = parseFloat(latest[2]);

var alerts = checkFreezerSafety(freezerTemp, shedTemp);

if (alerts.length > 0) {
  console.log("Freezer Alerts:");
  for (var i = 0; i < alerts.length; i++) {
    console.log("  " + alerts[i]);
  }
} else {
  console.log("Freezer status: Normal (" + freezerTemp + "°F in freezer, " + shedTemp + "°F in shed)");
}

We've caught two near-failures with this script. Once the compressor relay was sticking, and once the shed door seal failed and outdoor air was raising the ambient temperature enough to stress the compressor. Both times the alert came before any food was at risk.

---

The AI Layer: When Simple Logic Isn't Enough

The scripts above are mostly deterministic. Temperature thresholds, rate calculations, simple comparisons. The AI layer comes in when I need pattern recognition across multiple data sources, or when the question is too fuzzy for a conditional statement.

Examples from the last year:

"Based on my solar production, propane usage, and weather patterns from last November, what should I budget for energy costs this November?" I fed all three datasets plus historical weather data into a prompt. The model estimated within $40 of my actual costs. Not perfect, but better than my gut feeling.

"My generator has run 847 hours since its last oil change. Given the temperature and load patterns in the log, should I change oil now or can it wait two more weeks?" This is the kind of question where manufacturer guidelines say every 200 hours, but real-world conditions matter. Cold starts in extreme cold are harder on oil than steady running at moderate temps. The model correctly identified that most of my recent runtime was sustained load at moderate temperatures, and waiting two weeks was reasonable.

"Compare my water usage patterns for January through March over the past two years. Is there a leak developing?" This one caught a slow drip in the well house that was too small to notice on any single day's readings but showed up as a gradual increase in baseline usage. The model spotted it before I did.

The implementation for these multi-source analyses looks like this:

var fs = require("fs");
var https = require("https");

function gatherHomesteadData() {
  var data = {};

  var sources = [
    { name: "solar", path: "/home/pi/solar/recent_cycles.csv" },
    { name: "propane", path: "/home/pi/propane/levels.csv" },
    { name: "water", path: "/home/pi/sensors/well_pressure.csv" },
    { name: "generator", path: "/home/pi/generator/runtime.csv" },
    { name: "weather", path: "/home/pi/weather/station_log.csv" }
  ];

  for (var i = 0; i < sources.length; i++) {
    try {
      var raw = fs.readFileSync(sources[i].path, "utf8");
      var lines = raw.trim().split("\n");
      // Take last 200 readings to stay within token limits
      var recent = lines.slice(Math.max(1, lines.length - 200));
      data[sources[i].name] = lines[0] + "\n" + recent.join("\n");
    } catch (err) {
      data[sources[i].name] = "No data available";
    }
  }

  return data;
}

function askHomesteadAI(data, question, callback) {
  var systemPrompt = "You are an expert advisor for off-grid homesteading in interior Alaska. ";
  systemPrompt += "You understand solar power systems, propane heating, well water systems, ";
  systemPrompt += "generators, and the challenges of maintaining infrastructure in extreme cold. ";
  systemPrompt += "Analyze the provided sensor data and give specific, practical advice.";

  var userContent = "Sensor data from my homestead:\n\n";
  var keys = Object.keys(data);
  for (var i = 0; i < keys.length; i++) {
    userContent += "--- " + keys[i].toUpperCase() + " ---\n" + data[keys[i]] + "\n\n";
  }
  userContent += "Question: " + question;

  var payload = JSON.stringify({
    model: "gpt-4o-mini",
    messages: [
      { role: "system", content: systemPrompt },
      { role: "user", content: userContent }
    ],
    max_tokens: 1000
  });

  var options = {
    hostname: "api.openai.com",
    path: "/v1/chat/completions",
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": "Bearer " + process.env.OPENAI_API_KEY
    }
  };

  var req = https.request(options, function(res) {
    var body = "";
    res.on("data", function(chunk) { body += chunk; });
    res.on("end", function() {
      try {
        var result = JSON.parse(body);
        callback(null, result.choices[0].message.content);
      } catch (err) {
        callback(err);
      }
    });
  });

  req.on("error", function(err) { callback(err); });
  req.write(payload);
  req.end();
}

var homesteadData = gatherHomesteadData();
askHomesteadAI(homesteadData, "Based on current trends, what maintenance should I prioritize this month?", function(err, answer) {
  if (err) {
    console.error("Error:", err.message);
    return;
  }
  console.log("\nHomestead AI Advisor:\n");
  console.log(answer);
});

The key insight is keeping the data window manageable. You don't need to send a year of five-minute interval readings. The last 200 data points from each source is usually enough context for the model to identify patterns without hitting token limits or racking up costs.

---

Practical Lessons

After a year of running these scripts, here's what I've learned:

Start with the CSV. Don't build an elaborate IoT platform. A Raspberry Pi, some cheap sensors, and CSV files are enough. The AI scripts work fine on flat files. You can always build something fancier later, but you probably won't need to.

Ask specific questions. "Analyze my data" gets vague responses. "Is this pressure drop pattern consistent with ice blockage or mechanical failure?" gets useful answers. Treat the model like a knowledgeable friend who needs context.

Calibrate your adjustments. The propane temperature factor took a full winter to get right. Don't trust the first coefficient you pick. Log your predictions against actuals and adjust.

Run scripts on cron, not manually. I have a daily check that runs at 6 AM and sends me a summary email. The freezer monitor runs every 15 minutes. The propane forecast runs weekly. Automate the routine stuff so the alerts come to you.

Keep costs under control. GPT-4o-mini is cheap enough that daily homestead queries cost me less than a dollar a month. I was worried about API costs when I started. Turned out to be a non-issue for this use case.

Don't over-automate. I tried connecting the AI analysis to automated actions, like adjusting the solar charge controller settings based on weather predictions. Bad idea. The model is great at analysis and terrible at deciding when to override your hardware. Keep it advisory. You make the decisions.

The scripts are not replacing good homesteading sense. My neighbor Dave has been living out here for thirty years without a single sensor or line of code, and his place runs fine. But Dave has thirty years of calibrated intuition about his specific property. I've been here three years. The AI scripts are helping me build that intuition faster by making the patterns in my data visible instead of hidden in spreadsheets I never look at.

---

What's Next

I'm working on a seasonal preparation script that combines historical data from my homestead with long-range weather forecasts to generate a month-by-month maintenance checklist. The idea is to front-load maintenance before conditions make it difficult. Nobody wants to be replacing a well pump relay at thirty below when they could have caught the degradation pattern in October.

I'm also experimenting with image analysis for structural inspections. Taking photos of the roof, foundation, and well house and running them through a vision model to flag potential issues. Early results are mixed, but the technology is improving fast enough that I think this will be genuinely useful within a year.

The broader point is this: AI doesn't have to be about building SaaS products or enterprise solutions. Sometimes it's about keeping your pipes from freezing and knowing when to order propane. The simplest application of these tools has been one of the most personally valuable.

Shane Larson is a software engineer and the founder of Grizzly Peak Software. He writes about API development, AI applications, and software architecture from his cabin in Alaska. His book on training large language models is available on Amazon.