Jorge Ángel González Ordiano

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.

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

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.

Numerical Weather Prediction Data Free Solar Power Forecasting with Neural Networks

The worldwide increase in renewable energy penetration levels has made accuracy, availability, and affordability of wind and solar energy forecasting systems an integral part of the modern power grids. The present paper describes an approach to forecasting one-day-ahead photovoltaic (PV) power generation without the use of numerical weather prediction (NWP) data. The presented approach uses a closed loop non-linear autoregressive artificial neural network (CL-NAR-ANN) model with only the historical generated PV power data as input. In case of emergency, if the communication channel with the weather provider fails, the whole forecasting system runs a risk of failing. Also, purchasing NWP data might be too expensive for smaller utilities. In such situations, NWP data free models can provide cost-effective and reasonably accurate PV power forecasts, which can act as a good backup solution. Furthermore, the model is evaluated using a dataset from the Global Energy Forecasting Competition of 2014 (GEFCom14) and its results are compared to other data-driven models such as polynomial and artificial neural network (ANN) models with and without NWP data as input. The results suggest that the CL-NAR-ANN model delivers acceptable forecasts and outperforms other NWP free models by a margin of 8% in terms of root mean square error, hence supporting the possibility of obtaining acceptable forecasts using the CL-NAR-ANN.

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.

Evaluation of the Droplet-Microarray Platform for High-Throughput Screening of Suspension Cells.

Phenotypic cell-based high-throughput screenings play a central role in drug discovery and toxicology. The main tendency in cell screenings is the increase of the throughput and decrease of reaction volume in order to accelerate the experiments, reduce the costs, and enable screenings of rare cells. Conventionally, cell-based assays are performed in microtiter plates, which exist in 96- to 1536-wells formats and cannot be further miniaturized. In addition, performing screenings of suspension cells is associated with risk of losing cell content during the staining procedures and incompatibility with high-content microscopy. Here, we evaluate the Droplet-Microarray screening platform for culturing, screening, and imaging of suspension cells. We demonstrate pipetting-free cell seeding and proliferation of cells in individual droplets of 3–80 nL in volume. We developed a methodology to perform parallel treatment, staining, and fixation of suspension cells in individual droplets. Automated imaging of live suspension cells directly in the droplets combined with algorithms for pattern recognition for image analysis is demonstrated. We evaluated the developed methodology by performing a dose–response study with antineoplastic drugs. We believe that the DMA screening platform carries great potential to be adopted for broad spectrum of screenings of suspension cells.