Uncertainty-Aware Joint Generative Modeling of Fault Event Sequences and Sensor Time Series in Solar PV Plants

Jiayu Huang; Boyang Xu; Yongming Liu; Hao Yan

cs.LGNov 1, 2026

Uncertainty-Aware Joint Generative Modeling of Fault Event Sequences and Sensor Time Series in Solar PV Plants

Authors: Jiayu Huang, Boyang Xu, Yongming Liu, Hao Yan

Organizations: Arizona State University, Tempe, AZ, USA

Abstract

This paper presents a multimodal generative model designed to simulate a solar energy plant system for predictive maintenance and fault prediction. We propose a novel approach to enhance system generation through conditional generation by leveraging both event sequences and time-series data and improve next event prediction. The framework employs a Transformer Hawkes Process (THP) to encode event data and an iTransformer to encode time-series data, integrated via a co-attention mechanism that effectively captures cross-modal dependencies between discrete events and continuous signals. From these integrated representations, we employ two specialized decoders for simulation: a Conditional Latent Diffusion Model (CLDM) for modeling future sensor trajectories and the Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process (SMURF-THP) for high-fidelity event prediction. This dual-decoder strategy supports uncertainty-aware, history-conditioned simulation by rolling the model forward for a specified number of generation iterations, producing finite stochastic scenarios rather than deterministic extrapolations over the full observational record. Evaluated on a comprehensive dataset from the Red Rock Solar Site in Arizona, our approach improves aggregate event-prediction F1 relative to baselines and supports applications in predictive maintenance, fault detection, and energy optimization for solar power systems.

Figures & tables

Fig. 1: Overall encoder–decoder architecture: iTransformer and THP fused by co-attention; CLDM and SMURF-THP decoders.

Data Level	No. of Variables	Examples
System Level	47	curtail, weather, feeder
Inverter Level	11	power output (current, voltage)
Module Level	3	status, T-IGBT

TABLE I: Variables at Different Operational Levels of the PV Plant

Fig. 2: Power-signal forecast on a representative test sample (more samples in Supp. Section S3 ).

Fault Type	Baseline Models		Proposed Model
Fault Type	RNNPP	Event-only	Proposed Model
4	0.0000	0.0000	0.0000
18	0.5000	0.5000	0.5556
19	0.0930	0.1138	0.2017
36	0.1636	0.1250	0.1839
40	0.5766	0.2500	0.5326

TABLE II: Per-type next-event F1: baselines vs. proposed model.

Fig. 3: Five-day origin-aggregated next-event counting processes (true dashed, predicted solid) for types 19, 40, 42, and 67.

Metric / Type	CLDM+MLP	Event-only	Proposed	Supp.
Overall
TS MSE	0.4631	–	0.3522	–
TS MAE	0.4244	–	0.3418	–
Micro F1	0.0462	0.2209	0.3390	584
Macro F1	0.0200	0.2627	0.3323	584
Weighted F1	0.0326	0.2299	0.3565	584

TABLE III: Ablation: overall metrics and per-class F1 (CLDM+MLP, Event-only, Proposed). “–”: N/A; Supp.: test support.

Fig. S1: Finite-step rollout structure. At each iteration, only the available history (context window of time series and last $K$ events up to current time $t$ ) is used to predict the next event and to generate the time series segment up to the predicted event time, capped by the seven-day prediction window; then the window is rolled forward. The number of generation iterations $N_{\mathrm{iter}}$ is specified, while the calendar duration is induced by generated inter-event intervals and the per-iteration seven-day cap.

Fig. S2: Supplementary rollout flowchart. The rollout initializes from the available history, predicts or samples the next event, generates the sensor segment capped by the seven-day prediction window, updates the history, and repeats until the specified number of generation iterations $N_{\mathrm{iter}}$ is reached.

Fig. S3: Multi-day rollout visualization showing iterative event prediction and time series generation.

Fig. S4: Multi-step rollout error accumulation over $N_{\mathrm{iter}}=8$ iterations. The blue curve shows per-iteration MSE and the orange curve shows cumulative MSE relative to ground truth; shaded regions denote the standard deviation across samples. Cumulative MSE remains stable near $0.30$ without exponential growth, indicating controlled trajectory drift under event-driven alignment.

Fig. S5: Additional seven-day CLDM power-signal generation examples on six randomly selected test samples. Blue: ground truth; red: predicted mean; shaded red: sample-based uncertainty bands. Each panel reports the sample index and MSE.

Fig. S6: Mean predictive uncertainty (averaged across variables) over a 7-day horizon on the test set.

Parameter	Range
Learning rate	$[5\times 10^{-5},\,3\times 10^{-4}]$
Number of event types	14
Training sampler	Event-balanced
Batch size	128
Maximum epochs	$\{80,100,120\}$
Early-stopping patience	$\{15,25\}$

TABLE S1: Common hyperparameter settings for all models.

Hyperparameter	Candidate values
Time-series loss weight, $w_{\mathrm{ts}}$	$\{0.5,0.7,1.0\}$
Projection loss weight, $w_{\mathrm{proj}}$	$\{1.0,1.5,2.0\}$
Number of latent diffusion steps	$\{100,200\}$
Event classification loss weight, $w_{\mathrm{event}}$	$\{0.5,1.0,2.0\}$
Next-event loss weight, $w_{\mathrm{next}}$	$\{0.05,0.1,0.2\}$
Focal-loss parameter, $\gamma$	$\{0.0,1.0,2.0\}$

TABLE S2: Hyperparameter search space for the proposed model.

Parameter	Range
Learning rate	$[5\times 10^{-5},\,3\times 10^{-4}]$
Event loss weight, $w_{\mathrm{event}}$	$\{0.5,1.0,2.0\}$
Next-event loss weight, $w_{\mathrm{next}}$	$\{0.05,0.1,0.2\}$
Focal-loss parameter, $\gamma$	$\{0,1,2\}$
Event-class weighting	{Inv. sqrt., Inv.}
Sample-weight exponent, $\alpha$	$\{0.5,0.75,1.0\}$

TABLE S3: Hyperparameter search space for the THP baseline.

Parameter	Range
Time-series loss weight, $w_{\mathrm{ts}}$	$\{0.5,0.7,1.0\}$
Projection loss weight, $w_{\mathrm{proj}}$	$\{1.0,1.5,2.0\}$
Latent diffusion steps	$\{100,200\}$
Event loss weight, $w_{\mathrm{event}}$	$\{0.5,1.0,2.0\}$
Next-event loss weight, $w_{\mathrm{next}}$	$\{0.05,0.1,0.2\}$
Focal-loss parameter, $\gamma$	$\{0,1,2\}$

TABLE S4: Hyperparameter search space for the CLDM+MLP baseline.

Parameter	Range
Time-series loss weight, $w_{\mathrm{ts}}$	0 (fixed)
Event loss weight, $w_{\mathrm{event}}$	$\{0.5,1.0,2.0\}$
Next-event loss weight, $w_{\mathrm{next}}$	$\{0.05,0.1,0.2\}$
Focal-loss parameter, $\gamma$	$\{0,1,2\}$
Event-class weighting	{Inv. sqrt., Inv.}
Sample-weight exponent, $\alpha$	$\{0.5,0.75,1.0\}$

TABLE S5: Hyperparameter search space for the event-only model.

Parameter	Range
Time-series loss weight, $w_{\mathrm{ts}}$	0 (fixed)
Event loss weight, $w_{\mathrm{event}}$	$\{0.5,1.0,2.0\}$
Next-event loss weight, $w_{\mathrm{next}}$	$\{0.05,0.1,0.2\}$
Focal-loss parameter, $\gamma$	$\{0,1,2\}$
Event-class weighting	{Inv. sqrt., Inv.}
Sample-weight exponent, $\alpha$	$\{0.5,0.75,1.0\}$

TABLE S6: Hyperparameter search space for the RNNPP baseline.

Method	Conditioning	Scope / design
Event sequences (TPP): diffusion / flow
ADD-THIN [ 49 ]	Event history	Full-sequence denoising via add-and-thin; handles discrete+continuous components
DLTPP [ 52 ]	History (mapped)	Non-autoregressive long-horizon; bidirectional map between sequences and Euclidean space
Zeng et al. (CDiff) [ 50 ]	Event history	Interacting diffusion processes for inter-arrival times and types (sequence-to-sequence)
ADiff4TPP [ 51 ]	Event history	Asynchronous noise schedule to strengthen long-horizon conditioning
EventFlow [ 53 ]	Event history	Flow-matching-based forecasting for continuous-time event sequences

TABLE S7: Representative methods for event diffusion/flow, joint event–time-series co-prediction, and time-series forecasting (Transformers and diffusion/score-based).

Fig. S7: Comparison between vanilla Transformer (top) and the iTransformer (bottom): normalized time series from five randomly chosen sensors over 7 days, segmented into 96 time intervals per day. The iTransformer treats each variate as a token and applies attention across sensors.

Fig. S8: Co-attention Layer Architecture [ 9 ] . The bidirectional cross-attention mechanism enables each modality to attend to the other, capturing cross-modal interdependencies.

Fault type	Description
4	Over current S/W
12	Multiple issues
18	Multiple issues
19	AC Overcurrent issue
36	High grid frequency
40	Homopolar current Fault

TABLE S8: Fault Codes’ Descriptions

Event Start Time	Gap Time (mins)	Event Type	Duration (mins)
2019-06-24 11:53:10	0	55	17.06
2019-06-24 14:01:18	128.13	18	401.73
2019-06-30 18:18:14	8896.93	55	1736.66
2019-07-02 05:29:34	2111.33	55	3979.33
2019-07-05 05:29:26	4319.86	55	1065.73
2019-07-06 05:41:50	1452.39	55	2406.93

TABLE S9: Processed Real-Time Fault Data Sample

Metric	Value
Total / Trainable Parameters	171,086,433 ( $\sim$ 171.1 M)
Peak GPU Memory Footprint	8,241 MiB ( $\sim$ 8.2 GB)
Training Time per Epoch	1m 11.97s
Total Epochs	120
Estimated Total Training Time	$\sim$ 2 hours 24 minutes

TABLE S10: Computational Efficiency and Model Footprint

Item	Setting
Event context length	10 previous events
Time-series lookback	3 months
Prediction window	7 days after forecast origin
Event-time variable	$x=\log\tau$
Loss weights	$0.7(\mathcal{L}_{\mathrm{diff}}+1.5\mathcal{L}_{\mathrm{proj}})+2.0\mathcal{L}_{\mathrm{event}}+0.1\mathcal{L}_{\mathrm{time}}$
CLDM samples / steps	20 samples, 50 diffusion steps (uncertainty eval.)

TABLE S11: Reproducibility settings fixed in the case study

Time Series Generation

Time series generation creates synthetic sequential data that preserve temporal patterns, cross-variable structure, and controllable conditions.

See all 97 papers

Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting
May 27, 2026Kourosh Kiani, S. M. MuyeenMultivariate Time Series Forecasting Time Series Forecasting
1Electrical and Computer Engineering, Semnan University, Semnan, Iran · Electrical Engineering Department, Qatar University, Doha, Qatar
OutageDiT: A Foundation Model for Power Outage Forecasting and Scenario Generation
Sep 1, 2026Yunqin Zhu, Feng Qiu, Yao XieConditional Generative Modeling Time-Series Foundation Models
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology · Argonne National Laboratory
FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
Aug 6, 2026Yann Fabel, Bijan Nouri, Milon Miah +4Solar Irradiance Forecasting Time Series Forecasting
1DLR, Institute of Solar Research · 2RWTH Aachen University · 3CIEMAT, Energy Department, Renewable Energy Division

May 27, 2026J/K move · Enter open · S save

Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting

Kourosh Kiani, S. M. Muyeen

1Electrical and Computer Engineering, Semnan University, Semnan, Iran · Electrical Engineering Department, Qatar University, Doha, Qatar

Multivariate Time Series Forecasting Time Series Forecasting Solar Power Forecasting Conditional Diffusion Models

Open paper AskPDF arXiv

In this paper, we propose a novel conditional diffusion-based framework for multivariable time-series solar power forecasting. The proposed method reformulates temporal PV data as structured two-dimensional representations (images) using a sliding-window patch construction, enabling the application of Denoising Diffusion Probabilistic Models (DDPM) within a unified spatiotemporal learning paradigm. A key contribution of this work is the formulation of solar forecasting as an inpainting problem, where future time steps are treated as missing regions to be reconstructed. This is achieved through a mask-based conditional diffusion mechanism, in which historical observations are preserved as conditioning context while the target (future) region is progressively corrupted and subsequently recovered via reverse diffusion. The model learns to generate coherent future sequences conditioned on observed data, effectively performing time-series inpainting. To fully utilize all available features and ensure compatibility with U-Net architectural constraints, a zero-padding strategy is introduced to construct fixed-size inputs. The model is trained using a supervised denoising objective to predict injected noise, enabling accurate iterative reconstruction during the reverse process. Extensive experiments conducted on benchmark PV dataset, including GEFCom2014, demonstrate that the proposed approach achieves high forecasting accuracy, particularly for short-term horizons. The results highlight the effectiveness of integrating diffusion-based generative modeling with an inpainting formulation for robust, flexible, and high-fidelity solar power forecasting.

Abstract

Time Series Generation

Explore similar work

Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting

OutageDiT: A Foundation Model for Power Outage Forecasting and Scenario Generation

FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting