7 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
Smart Charging of Electric Vehicles with Cloud-based Optimization and a Lightweight User Interface – A Real-World Application in the Energy Lab 2.0: Poster
Smart Charging (SC) of Electric Vehicles (EVs) integrates them into the power system to support grid stability by power management. Large-scale adoption of SC requires a high level of EV user acceptance. Therefore, it is imperative to make the underlying charging scheme tangible for the user. We propose a web app for the user to start, adjust and monitor the charging process via a User Interface (UI). We outline the integration of this web app into an Internet of Things (IoT) architecture to establish communication with the charging station. Two scenarios demonstrate the operation of the system. Future field studies on SC should involve the EV user due to individual preferences and responses to incentive schemes. Therefore, we propose the Smart Charging Wizard with a customizable UI and optimization module for future research and collaborative development
Review of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting
Review of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in different
domains such as energy systems and economics. Creating a forecasting model for
a specific use case requires an iterative and complex design process. The
typical design process includes the five sections (1) data pre-processing, (2)
feature engineering, (3) hyperparameter optimization, (4) forecasting method
selection, and (5) forecast ensembling, which are commonly organized in a
pipeline structure. One promising approach to handle the ever-growing demand
for time series forecasts is automating this design process. The present paper,
thus, analyzes the existing literature on automated time series forecasting
pipelines to investigate how to automate the design process of forecasting
models. Thereby, we consider both Automated Machine Learning (AutoML) and
automated statistical forecasting methods in a single forecasting pipeline. For
this purpose, we firstly present and compare the proposed automation methods
for each pipeline section. Secondly, we analyze the automation methods
regarding their interaction, combination, and coverage of the five pipeline
sections. For both, we discuss the literature, identify problems, give
recommendations, and suggest future research. This review reveals that the
majority of papers only cover two or three of the five pipeline sections. We
conclude that future research has to holistically consider the automation of
the forecasting pipeline to enable the large-scale application of time series
forecasting
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
Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles
Smart charging of Electric Vehicles (EVs) reduces operating costs, allows
more sustainable battery usage, and promotes the rise of electric mobility. In
addition, bidirectional charging and improved connectivity enables efficient
power grid support. Today, however, uncoordinated charging, e.g. governed by
users' habits, is still the norm. Thus, the impact of upcoming smart charging
applications is mostly unexplored. We aim to estimate the expenses inherent
with smart charging, e.g. battery aging costs, and give suggestions for further
research. Using typical on-board sensor data we concisely model and validate an
EV battery. We then integrate the battery model into a realistic smart charging
use case and compare it with measurements of real EV charging. The results show
that i) the temperature dependence of battery aging requires precise thermal
models for charging power greater than 7 kW, ii) disregarding battery aging
underestimates EVs' operating costs by approx. 30%, and iii) the profitability
of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g.
energy arbitrage, depends on battery aging costs and the electricity price
spread.Comment: Submitted to IEEE Transaction on Smart Gri