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@ -16,10 +16,10 @@ static const char *TAG = "smart-oil-heater-control-system-inputs";
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const uint8_t uBurnerFaultPin = 19U;
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const uint8_t uDS18B20Pin = 4U;
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const onewire_addr_t uChamperTempSensorAddr = 0x3e0000001754be28;
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const onewire_addr_t uOutdoorTempSensorAddr = 0x880000001648e328;
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const onewire_addr_t uInletFlowTempSensorAddr = 0xe59cdef51e64ff28;
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const onewire_addr_t uReturnFlowTempSensorAddr = 0xa7a8e1531f64ff28;
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const onewire_addr_t uChamperTempSensorAddr = 0xd00000108cd01d28;
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const onewire_addr_t uOutdoorTempSensorAddr = 0x78000000c6c2f728;
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const onewire_addr_t uInletFlowTempSensorAddr = 0x410000108b8c0628;
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const onewire_addr_t uReturnFlowTempSensorAddr = 0x90000108cc77c28;
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onewire_addr_t uOneWireAddresses[MAX_DN18B20_SENSORS];
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float fDS18B20Temps[MAX_DN18B20_SENSORS];
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@ -36,7 +36,7 @@ void taskInput(void *pvParameters);
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void initMeasurement(sMeasurement *pMeasurement);
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void updateAverage(sMeasurement *pMeasurement);
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void updatePrediction(sMeasurement *pMeasurement);
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float linearRegressionPredict(const float *samples, size_t count, float futureIndex);
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float linearRegressionPredict(const float *samples, size_t count, size_t bufferIndex, float futureIndex);
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void initInputs(void)
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{
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@ -162,6 +162,7 @@ void updatePrediction(sMeasurement *pMeasurement)
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predict60s->fValue = linearRegressionPredict(
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predict60s->samples,
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predict60s->bufferCount,
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predict60s->bufferIndex,
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predict60s->bufferCount + 60.0f);
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}
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@ -267,7 +268,7 @@ void taskInput(void *pvParameters)
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}
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}
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float linearRegressionPredict(const float *samples, size_t count, float futureIndex)
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float linearRegressionPredict(const float *samples, size_t count, size_t bufferIndex, float futureIndex)
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{
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if (count == 0)
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return 0.0f; // No prediction possible with no data
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@ -276,8 +277,11 @@ float linearRegressionPredict(const float *samples, size_t count, float futureIn
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for (size_t i = 0; i < count; i++)
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{
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float x = (float)i; // Time index
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float y = samples[i]; // Sample value
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// Calculate the circular buffer index for the current sample
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size_t circularIndex = (bufferIndex + i + 1) % count;
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float x = (float)i; // Time index
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float y = samples[circularIndex]; // Sample value
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sumX += x;
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sumY += y;
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@ -287,8 +291,8 @@ float linearRegressionPredict(const float *samples, size_t count, float futureIn
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// Calculate slope (m) and intercept (b) of the line: y = mx + b
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float denominator = (count * sumX2 - sumX * sumX);
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if (fabs(denominator) < 1e-6) // Avoid division by zero
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return samples[count - 1]; // Return last value as prediction
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if (fabs(denominator) < 1e-6) // Avoid division by zero
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return samples[bufferIndex]; // Return the latest value as prediction
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float m = (count * sumXY - sumX * sumY) / denominator;
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float b = (sumY - m * sumX) / count;
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