14 research outputs found
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
Accurate PhotoVoltaic (PV) power generation forecasting is vital for the
efficient operation of Smart Grids. The automated design of such accurate
forecasting models for individual PV plants includes two challenges: First,
information about the PV mounting configuration (i.e. inclination and azimuth
angles) is often missing. Second, for new PV plants, the amount of historical
data available to train a forecasting model is limited (cold-start problem). We
address these two challenges by proposing a new method for day-ahead PV power
generation forecasts called AutoPV. AutoPV is a weighted ensemble of
forecasting models that represent different PV mounting configurations. This
representation is achieved by pre-training each forecasting model on a separate
PV plant and by scaling the model's output with the peak power rating of the
corresponding PV plant. To tackle the cold-start problem, we initially weight
each forecasting model in the ensemble equally. To tackle the problem of
missing information about the PV mounting configuration, we use new data that
become available during operation to adapt the ensemble weights to minimize the
forecasting error. AutoPV is advantageous as the unknown PV mounting
configuration is implicitly reflected in the ensemble weights, and only the PV
plant's peak power rating is required to re-scale the ensemble's output. AutoPV
also allows to represent PV plants with panels distributed on different roofs
with varying alignments, as these mounting configurations can be reflected
proportionally in the weighting. Additionally, the required computing memory is
decoupled when scaling AutoPV to hundreds of PV plants, which is beneficial in
Smart Grids with limited computing capabilities. For a real-world data set with
11 PV plants, the accuracy of AutoPV is comparable to a model trained on two
years of data and outperforms an incrementally trained model
Transformer Training Strategies for Forecasting Multiple Load Time Series
In the smart grid of the future, accurate load forecasts on the level of
individual clients can help to balance supply and demand locally and to prevent
grid outages. While the number of monitored clients will increase with the
ongoing smart meter rollout, the amount of data per client will always be
limited. We evaluate whether a Transformer load forecasting model benefits from
a transfer learning strategy, where a global univariate model is trained on the
load time series from multiple clients. In experiments with two datasets
containing load time series from several hundred clients, we find that the
global training strategy is superior to the multivariate and local training
strategies used in related work. On average, the global training strategy
results in 21.8% and 12.8% lower forecasting errors than the two other
strategies, measured across forecasting horizons from one day to one month into
the future. A comparison to linear models, multi-layer perceptrons and LSTMs
shows that Transformers are effective for load forecasting when they are
trained with the global training strategy
Non-Sequential Machine Learning Pipelines with pyWATTS
pyWATTS is an open-source Python-based workflow automation tool for time series analysis. pyWATTS simplifies the evaluation process and the design of repetitive machine learning experiments with time series by providing a powerful pipeline solution including preprocessing and analysis modules. Unlike existing sequential pipeline solutions, pyWATTS enables more complex and non-sequential pipelines. Such non-sequential pipelines are beneficial, for example, in forecasting electrical load time series, detecting anomalies in time series, or generating synthetic time series. This talk presents the basic ideas of pyWATTS, the current features, and existing use cases. It also gives an outlook on the future developments of pyWATTS and the cooperation with sktime
High-speed quantum cascade detector characterized with a mid-infrared femtosecond oscillator
Quantum cascade detectors (QCD) are photovoltaic mid-infrared detectors based on intersubband transitions. Owing to the sub-picosecond carrier transport between subbands and the absence of a bias voltage, QCDs are ideally suited for high-speed and room temperature operation. Here, we demonstrate the design, fabrication, and characterization of 4.3 µm wavelength QCDs optimized for large electrical bandwidth. The detector signal is extracted via a tapered coplanar waveguide (CPW), which was impedance-matched to 50 Ω. Using femtosecond pulses generated by a mid-infrared optical parametric oscillator (OPO), we show that the impulse response of the fully packaged QCDs has a full-width at half-maximum of only 13.4 ps corresponding to a 3-dB bandwidth of more than 20 GHz. Considerable detection capability beyond the 3-dB bandwidth is reported up to at least 50 GHz, which allows us to measure more than 600 harmonics of the OPO repetition frequency reaching 38 dB signal-to-noise ratio without the need of electronic amplification.ISSN:1094-408