Self-Supervised Temporal Pattern Mining for smart agriculture microgrid orchestration under multi-jurisdictional compliance

Self-Supervised Temporal Pattern Mining for smart agriculture microgrid orchestration under multi-jurisdictional compliance

Introduction: The Learning Journey That Sparked This Research

During my investigation of autonomous energy systems for sustainable agriculture, I encountered a problem that traditional supervised learning couldn't solve. I was working with a research consortium deploying smart microgrids across agricultural regions spanning three different regulatory jurisdictions. The challenge wasn't just optimizing energy distribution—it was discovering hidden temporal patterns in energy consumption, renewable generation, and regulatory constraints that changed with time, location, and jurisdiction.

One evening, while analyzing solar generation data from a vineyard in California's Central Valley, I made a crucial observation. The patterns weren't just daily or seasonal—they followed complex multi-scale rhythms influenced by irrigation schedules, crop growth stages, market electricity prices, and evolving carbon credit regulations. Traditional labeled datasets couldn't capture this complexity because the "right" patterns hadn't been defined yet. Through studying recent advances in self-supervised learning for time series, I realized we needed an approach that could discover these patterns autonomously while respecting jurisdictional boundaries.

This realization led me down a six-month research path exploring self-supervised temporal pattern mining specifically for agricultural microgrids operating under multi-jurisdictional compliance. What emerged was a framework that not only optimizes energy flows but discovers the underlying temporal structure of agricultural operations, energy markets, and regulatory environments.

Technical Background: The Convergence of Multiple Disciplines

The Core Problem Space

Smart agriculture microgrids represent one of the most complex optimization challenges in AI today. They operate at the intersection of:

1. Agricultural Temporal Dynamics: Crop growth cycles, irrigation schedules, harvesting periods
2. Energy System Physics: Power flow constraints, storage dynamics, renewable intermittency
3. Regulatory Compliance: Multiple jurisdictions with different rules for energy trading, carbon accounting, water rights
4. Market Economics: Real-time electricity pricing, carbon credit markets, agricultural commodity prices

While exploring temporal representation learning, I discovered that most existing approaches treat these dimensions separately. My experimentation revealed that the most valuable insights emerge from their interactions—how irrigation schedules shift in response to both soil moisture and electricity prices, or how carbon credit trading influences when farms draw from versus feed into the grid.

Self-Supervised Learning for Temporal Data

Self-supervised learning (SSL) has revolutionized how we extract representations from unlabeled data. In my research of temporal SSL specifically, I found three approaches particularly promising for agricultural microgrids:

Temporal Contrastive Learning: Creating positive pairs through temporal transformations (time warping, cropping, masking) and negative pairs from different time periods or locations.

Predictive Coding: Training models to predict future states or reconstruct masked portions of time series.

Temporal Clustering: Discovering natural groupings in temporal patterns without predefined labels.

One interesting finding from my experimentation with these techniques was that agricultural energy patterns exhibit multi-scale periodicity—daily irrigation cycles, weekly market patterns, monthly billing cycles, and annual growing seasons—all interacting simultaneously.

Implementation Framework: Building the Pattern Discovery System

Architecture Overview

The system I developed consists of four interconnected modules:

1. Temporal Encoder Network: Learns representations from multi-modal time series
2. Pattern Mining Engine: Discovers recurring temporal motifs
3. Compliance Constraint Layer: Encodes jurisdictional rules as differentiable constraints
4. Orchestration Optimizer: Makes real-time decisions using discovered patterns

Core Implementation: Temporal Encoder with Multi-Scale Attention

During my investigation of transformer architectures for time series, I found that standard positional encodings failed to capture agricultural rhythms. I developed a Seasonal Positional Encoding that explicitly models known periodicities while learning unknown ones.

import torch
import torch.nn as nn
import numpy as np

class SeasonalPositionalEncoding(nn.Module):
    """Positional encoding that captures agricultural temporal rhythms"""

    def __init__(self, d_model, max_len=10000, known_periods=None):
        super().__init__()
        self.d_model = d_model

        # Known agricultural periods (in hours)
        if known_periods is None:
            known_periods = [24, 168, 720, 2160, 8760]  # day, week, month, season, year

        self.known_periods = known_periods
        self.learned_periods = nn.Parameter(torch.randn(5) * 2)  # Learn additional periods

    def forward(self, x, timestamps):
        """
        x: [batch_size, seq_len, d_model]
        timestamps: [batch_size, seq_len] in hours from reference
        """
        batch_size, seq_len, _ = x.shape

        # Create encoding for each known period
        encodings = []
        for period in self.known_periods:
            # Sinusoidal encoding for each period
            angle = 2 * np.pi * timestamps / period
            sin_enc = torch.sin(angle).unsqueeze(-1)
            cos_enc = torch.cos(angle).unsqueeze(-1)
            encodings.extend([sin_enc, cos_enc])

        # Learned periodic encodings
        for i in range(len(self.learned_periods)):
            period = torch.sigmoid(self.learned_periods[i]) * 1000  # Scale to reasonable range
            angle = 2 * np.pi * timestamps / period
            sin_enc = torch.sin(angle).unsqueeze(-1)
            cos_enc = torch.cos(angle).unsqueeze(-1)
            encodings.extend([sin_enc, cos_enc])

        positional_encoding = torch.cat(encodings, dim=-1)

        # Project to model dimension if needed
        if positional_encoding.shape[-1] != self.d_model:
            proj = nn.Linear(positional_encoding.shape[-1], self.d_model)
            positional_encoding = proj(positional_encoding)

        return x + positional_encoding

Self-Supervised Pre-training Strategy

Through studying contrastive learning papers, I developed a multi-task self-supervised approach specifically for agricultural time series:

class AgriculturalSSL(nn.Module):
    """Self-supervised learning for agricultural temporal patterns"""

    def __init__(self, input_dim=10, hidden_dim=256, output_dim=128):
        super().__init__()

        # Multi-scale temporal encoder
        self.daily_encoder = TemporalConv(input_dim, hidden_dim, kernel_size=24)
        self.weekly_encoder = TemporalConv(input_dim, hidden_dim, kernel_size=168)
        self.seasonal_encoder = LSTMEncoder(input_dim, hidden_dim)

        # Projection heads for different SSL tasks
        self.contrastive_projection = nn.Sequential(
            nn.Linear(hidden_dim * 3, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )

        self.prediction_head = nn.Linear(hidden_dim * 3, input_dim)

    def temporal_contrastive_loss(self, anchor, positive, negatives):
        """InfoNCE loss for temporal patterns"""
        # Normalize embeddings
        anchor = F.normalize(anchor, dim=-1)
        positive = F.normalize(positive, dim=-1)
        negatives = F.normalize(negatives, dim=-1)

        # Positive similarity
        pos_sim = torch.sum(anchor * positive, dim=-1)

        # Negative similarities
        neg_sims = torch.matmul(anchor, negatives.T)

        # InfoNCE loss
        logits = torch.cat([pos_sim.unsqueeze(1), neg_sims], dim=1)
        labels = torch.zeros(logits.shape[0], dtype=torch.long, device=anchor.device)

        return F.cross_entropy(logits / 0.1, labels)

    def forward(self, x_daily, x_weekly, x_seasonal, timestamps):
        # Encode at different scales
        daily_repr = self.daily_encoder(x_daily)
        weekly_repr = self.weekly_encoder(x_weekly)
        seasonal_repr = self.seasonal_encoder(x_seasonal)

        # Combine representations
        combined = torch.cat([daily_repr, weekly_repr, seasonal_repr], dim=-1)

        # SSL tasks
        contrastive_repr = self.contrastive_projection(combined)
        predictions = self.prediction_head(combined)

        return contrastive_repr, predictions

Pattern Mining with Temporal Motif Discovery

One of my most significant discoveries came while experimenting with motif discovery algorithms. Traditional approaches assumed fixed-length patterns, but agricultural operations follow variable-length routines. I adapted the Matrix Profile algorithm for streaming agricultural data:

class AdaptiveMatrixProfile:
    """Adaptive motif discovery for agricultural time series"""

    def __init__(self, min_length=6, max_length=168, step_size=1):
        self.min_length = min_length  # Minimum pattern length (hours)
        self.max_length = max_length  # Maximum pattern length (1 week)
        self.step_size = step_size

    def find_motifs(self, time_series, exclusion_zone=None):
        """
        Find recurring temporal motifs of variable length
        """
        n = len(time_series)
        motifs = []

        # Try different window lengths
        for window_length in range(self.min_length, self.max_length + 1, self.step_size):
            if window_length > n:
                continue

            # Compute matrix profile for this window length
            mp, mp_index = self.compute_matrix_profile(time_series, window_length)

            # Find motif pairs (most similar subsequences)
            for i in range(len(mp)):
                if mp[i] < np.percentile(mp, 5):  # Top 5% most similar
                    j = mp_index[i]

                    # Check exclusion zone (avoid overlapping motifs)
                    if exclusion_zone is not None:
                        if abs(i - j) < exclusion_zone:
                            continue

                    motif = {
                        'length': window_length,
                        'positions': (i, j),
                        'similarity': 1 - mp[i],  # Convert distance to similarity
                        'pattern': time_series[i:i+window_length]
                    }
                    motifs.append(motif)

        # Cluster similar motifs across different lengths
        clustered_motifs = self.cluster_motifs(motifs)

        return clustered_motifs

    def compute_matrix_profile(self, ts, m):
        """Compute matrix profile for window length m"""
        n = len(ts)
        mp = np.full(n - m + 1, np.inf)
        mp_index = np.zeros(n - m + 1, dtype=int)

        # Simplified implementation - in practice use STUMPY or similar
        for i in range(n - m + 1):
            subsequence = ts[i:i+m]
            best_dist = np.inf
            best_idx = -1

            for j in range(n - m + 1):
                if abs(i - j) < m:  # Exclusion zone
                    continue

                dist = np.linalg.norm(subsequence - ts[j:j+m])
                if dist < best_dist:
                    best_dist = dist
                    best_idx = j

            mp[i] = best_dist
            mp_index[i] = best_idx

        return mp, mp_index

Compliance-Aware Orchestration System

Multi-Jurisdictional Constraint Encoding

The most challenging aspect of this research was encoding diverse regulatory constraints. While exploring constraint optimization techniques, I realized that compliance rules often have temporal dimensions—certain actions are only allowed during specific hours, or credits accumulate at different rates in different jurisdictions.

class JurisdictionalConstraintLayer:
    """Differentiable constraint layer for multi-jurisdictional compliance"""

    def __init__(self, jurisdictions):
        self.jurisdictions = jurisdictions

        # Learnable compliance parameters
        self.compliance_weights = nn.ParameterDict({
            j: nn.Parameter(torch.ones(24)) for j in jurisdictions
        })  # Hourly compliance weights for each jurisdiction

        self.penalty_scales = nn.ParameterDict({
            j: nn.Parameter(torch.tensor(1.0)) for j in jurisdictions
        })

    def forward(self, actions, timestamps, locations):
        """
        actions: [batch_size, num_actions]
        timestamps: [batch_size] in hours
        locations: [batch_size] jurisdiction IDs
        """
        batch_size = actions.shape[0]
        penalties = torch.zeros(batch_size)

        for i in range(batch_size):
            jurisdiction = locations[i]
            hour = int(timestamps[i] % 24)

            # Get compliance weight for this hour and jurisdiction
            weight = self.compliance_weights[jurisdiction][hour]
            penalty_scale = self.penalty_scales[jurisdiction]

            # Calculate constraint violations
            # Example: Energy export limits during peak hours
            if hour in [14, 15, 16, 17]:  # Peak afternoon hours
                export_limit = 0.5  # Max 50% of capacity can be exported
                export_violation = torch.relu(actions[i, 1] - export_limit)
                penalties[i] += penalty_scale * weight * export_violation

            # Carbon credit accumulation constraints
            if actions[i, 2] > 0:  # If generating carbon credits
                # Different jurisdictions have different credit rates
                credit_rate = self.get_credit_rate(jurisdiction, hour)
                expected_credits = actions[i, 2] * credit_rate
                # Penalize deviations from expected credit patterns
                credit_penalty = torch.abs(actions[i, 3] - expected_credits)
                penalties[i] += penalty_scale * weight * credit_penalty

        return penalties

    def get_credit_rate(self, jurisdiction, hour):
        """Get carbon credit accumulation rate based on jurisdiction and time"""
        # Learned rates based on historical compliance data
        rates = {
            'CA': [0.9, 0.9, 0.9, 0.9, 0.9, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
                   1.0, 1.0, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9],
            'AZ': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
                   0.7, 0.7, 0.7, 0.7, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
            'NV': [0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
                   1.0, 1.0, 0.6, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]
        }
        return rates.get(jurisdiction, [1.0] * 24)[hour]

Orchestration Optimization with Discovered Patterns

The final orchestration system integrates discovered patterns with real-time optimization:

python
class MicrogridOrchestrator:
    """Main orchestration system using discovered temporal patterns"""

    def __init__(self, pattern_miner, constraint_layer):
        self.pattern_miner = pattern_miner
        self.constraint_layer = constraint_layer
        self.pattern_memory = {}  # Store discovered patterns

    def orchestrate(self, current_state, forecast, timestamps, location):
        """
        Make orchestration decisions using discovered patterns
        """
        # Discover relevant patterns in current context
        context_patterns = self.discover_context_patterns(
            current_state, timestamps, location
        )

        # Build optimization problem
        decision = self.optimize_with_patterns(
            current_state, forecast, context_patterns, timestamps, location
        )

        # Update pattern memory with new experience
        self.update_pattern_memory(current_state, decision, timestamps)

        return decision

    def discover_context_patterns(self, state, timestamps, location):
        """Find patterns relevant to current context"""
        relevant_patterns = []

        # Check pattern memory for similar contexts
        for pattern_id, pattern in self.pattern_memory.items():
            similarity = self.calculate_context_similarity(
                pattern['context'], state, timestamps, location
            )

            if similarity > 0.7:  # Similarity threshold
                pattern['current_similarity'] = similarity
                relevant_patterns.append(pattern)

        # If no similar patterns found, mine new ones
        if not relevant_patterns:
            new_patterns = self.pattern_miner.find_motifs(
                state['historical_series']
            )
            relevant_patterns = self.filter_patterns_by_context(
                new_patterns, state, timestamps, location
            )

        return relevant_patterns

    def optimize_with_patterns(self, state, forecast, patterns, timestamps, location):
        """Optimize decisions using pattern-informed constraints"""

        # Initialize decision variables
        n_variables = 5  # [import, export, storage_charge, storage_discharge, curtailment]
        decisions = cp.Variable(n_variables)

        # Objective: Minim