4 research outputs found
An Unsupervised Deep Learning Approach for Scenario Forecasts
In this paper, we propose a novel scenario forecasts approach which can be
applied to a broad range of power system operations (e.g., wind, solar, load)
over various forecasts horizons and prediction intervals. This approach is
model-free and data-driven, producing a set of scenarios that represent
possible future behaviors based only on historical observations and point
forecasts. It first applies a newly-developed unsupervised deep learning
framework, the generative adversarial networks, to learn the intrinsic patterns
in historical renewable generation data. Then by solving an optimization
problem, we are able to quickly generate large number of realistic future
scenarios. The proposed method has been applied to a wind power generation and
forecasting dataset from national renewable energy laboratory. Simulation
results indicate our method is able to generate scenarios that capture spatial
and temporal correlations. Our code and simulation datasets are freely
available online.Comment: Accepted to Power Systems Computation Conference 2018 Code available
at https://github.com/chennnnnyize/Scenario-Forecasts-GA
Optimisation of residential battery integrated photovoltaics system: analyses and new machine learning methods
Modelling and optimisation of battery integrated photovoltaics (PV) systems require a certain amount of high-quality input PV and load data. Despite the recent rollouts of smart meters, the amount of accessible proprietary load and PV data is still limited.
This thesis addresses this data shortage issue by performing data analyses and proposing novel data extrapolation, interpolation, and synthesis models. First, a sensitivity analysis is conducted to investigate the impacts of applying PV and load data with various temporal resolutions in PV-battery optimisation models. The explored data granularities range from 5-second to hourly, and the analysis indicates 5-minute to be the most suitable for the proprietary data, achieving a good balance between accuracy and computational cost. A data extrapolation model is then proposed using net meter data clustering, which can extrapolate a month of 5-minute net/gross meter data to a year of data. This thesis also develops two generative adversarial networks (GANs) based models: a deep convolutional generative adversarial network (DCGAN) model which can generate PV and load power from random noises; a super resolution generative adversarial network (SRGAN) model which synthetically interpolates 5-minute load and PV power data from 30-minute/hourly data.
All the developed approaches have been validated using a large amount of real-time residential PV and load data and a battery size optimisation model as the end-use application of the extrapolated, interpolated, and synthetic datasets. The results indicate that these models lead to optimisation results with a satisfactory level of accuracy, and at the same time, outperform other comparative approaches. These newly proposed approaches can potentially assist researchers, end-users, installers and utilities with their battery sizing and scheduling optimisation analyses, with no/minimal requirements on the granularity and amount of the available input data