Simon Waczowicz

Photovoltaic power forecasting using simple data-driven models without weather data

The present contribution offers evidence regarding the possibility of obtaining reasonable photovoltaic power forecasts without using weather data and with simple data-driven models. The lack of weather data as input stems from the fact that the constant obtainment of forecast weather data might become too expensive or that communication with weather services might fail, but still accurate planning and scheduling decisions have to be conducted. Therefore, accurate one-day ahead forecasting models with only information of past generated power as input for offline photovoltaic systems or as backup in case of communication failures are of interest. The results contained in the present contribution, obtained using a freely available dataset, provide a baseline with which more complex forecasting models can be compared. Additionally, it will also be shown that the presented weather-free data-driven models provide better forecasts than a trivial persistence technique for different forecast horizons. The methodology used in the present work for the data preprocessing and the creation and validation of forecasting models has a generalization capacity and thus can be used for different types of time series as well as different data mining techniques.

Data Mining zur Analyse der Auswirkungen von Preissignalen auf Haushaltsstromkunden

Zusammenfassung Dieser Beitrag widmet sich der Analyse der Auswirkungen von Preissignalen auf das Verbrauchsverhalten von Haushaltsstromkunden. Er schlägt einen systematischen Auswerteprozess vor, der die Datenvorverarbeitung, verschiedene Aggregationsschritte, die Analyse mit Clusterverfahren (für preisbeeinflusste Typtage bei einzelnen oder aggregierten Haushalten) und die Analyse einzelner Abtastzeitpunkte umfasst. Dieser Auswerteprozess wird auf den Olympic Peninsula Project Datensatz angewendet.

A contribution to the load forecast of price elastic consumption behaviour

By influencing the demand side by means of price signals (Demand Response) additional flexibility potential in electric supply systems can be provided. However, by influencing the demand side typical consumption patterns of previously unaffected consumers are changed. This will lead to increasing uncertainty in load forecasting. This paper deals with the forecast of load time series in consideration of price-based consumption influence. Additional requirements for load forecasting methods resulting from the price elastic consumption behaviour are analysed in this paper. Furthermore, the model residuals of established model approaches will be analysed to explain the disturbance characteristic caused by the price elasticity. Finally, the impact of the model residuals on the load forecast was investigated.

Energy forecasting tools and services

The increasing complexity of the power grid and the continuous integration of volatile renewable energy systems on all aspects of it have made more precise forecasts of both energy supply and demand necessary for the future Smart Grid. Yet, the ever increasing volume of tools and services makes it difficult for users (e.g., energy utility companies) and researchers to obtain even a general sense of what each tool or service offers. The present contribution provides an overview and categorization of several energy‐related forecasting tools and services (specifically for load and volatile renewable power), as well as general information regarding principles of time series, load, and volatile renewable power forecasting. WIREs Data Mining Knowl Discov 2018, 8:e1235. doi: 10.1002/widm.1235

Prognose für preisbeeinflusstes Verbrauchsverhalten

Zusammenfassung Durch die Beeinflussung des elektrischen Verbrauchs mittels Preiszeitreihen (Demand Response) können zusätzliche Flexibilitätspotentiale für die Energieversorgung erschlossen werden. Durch die Einführung von Demand Response ergeben sich jedoch neue Anforderungen an die Lastprognose. Dieser Beitrag befasst sich mit der Modellierung von preisbeeinflusstem Verbrauchsverhalten (PVV) und stellt einen neuen Prognoseansatz vor.

Demand Response clustering: Automatically finding optimal cluster hyper-parameter values

Time series clustering methods, such as Fuzzy C-Means (FCM) noise clustering, can be efficiently used to obtain typical price-influenced load profiles (TPILPs) through the data-driven analysis and modelling of the consumption behaviour of household electricity customers in response to price signals (Demand Response, DR). However, the analysis of load time series with cluster methods presupposes that the user has a lot of experience in selecting good cluster hyper-parameter values (e.g. number of clusters or fuzzifier). The present contribution proposes a practical method to the automatic selection of optimal hyper-parameter values for DR clustering.

Assessment of Unsupervised Standard Pattern Recognition Methods for Industrial Energy Time Series

Finding and extracting standard patterns in energy time series is very important to many real-world applications. Hence, there exists a multitude of pattern recognition algorithms with a majority of them being supervised ones. The advantage of supervision is that it can easily be checked if the algorithm is performing well or not. However, if no labels are available, an unsupervised pattern search is necessary. This search is faced with the challenge of how to measure success. Thus the question arises, when is a found pattern -- for example a motif or a mean cluster curve -- really describing the standard behaviour of a process and not just some kind of irrelevant behaviour? The present paper introduces a new method to assess two methods -- namely clustering and motif discovery -- in their quest to find standard profiles in energy time series data from industrial processes. Although both methods share the same aim, the results are incongruent. This finding has profound implications for real-world applications.

Concept and benchmark results for Big Data energy forecasting based on Apache Spark

The present article describes a concept for the creation and application of energy forecasting models in a distributed environment. Additionally, a benchmark comparing the time required for the training and application of data-driven forecasting models on a single computer and a computing cluster is presented. This comparison is based on a simulated dataset and both R and Apache Spark are used. Furthermore, the obtained results show certain points in which the utilization of distributed computing based on Spark may be advantageous.