Short and long-term wind turbine power output prediction

In the wind energy industry, it is of great importance to develop models that accurately forecast the power output of a wind turbine, as such predictions are used for wind farm location assessment or power pricing and bidding, monitoring, and preventive maintenance. As a first step, and following the guidelines of the existing literature, we use the supervisory control and data acquisition (SCADA) data to model the wind turbine power curve (WTPC). We explore various parametric and non-parametric approaches for the modeling of the WTPC, such as parametric logistic functions, and non-parametric piecewise linear, polynomial, or cubic spline interpolation functions. We demonstrate that all aforementioned classes of models are rich enough (with respect to their relative complexity) to accurately model the WTPC, as their mean squared error (MSE) is close to the MSE lower bound calculated from the historical data. We further enhance the accuracy of our proposed model, by incorporating additional environmental factors that affect the power output, such as the ambient temperature, and the wind direction. However, all aforementioned models, when it comes to forecasting, seem to have an intrinsic limitation, due to their inability to capture the inherent auto-correlation of the data. To avoid this conundrum, we show that adding a properly scaled ARMA modeling layer increases short-term prediction performance, while keeping the long-term prediction capability of the model

Spatio-temporal prediction of wind fields

Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration.Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration

A novel framework for medium-term wind power prediction based on temporal attention mechanisms

Wind energy is a widely distributed, recyclable and environmentally friendly energy source that plays an important role in mitigating global warming and energy shortages. Wind energy's uncertainty and fluctuating nature makes grid integration of large-scale wind energy systems challenging. Medium-term wind power forecasts can provide an essential basis for energy dispatch, so accurate wind power forecasts are essential. Much research has yielded excellent results in recent years. However, many of them require additional experimentation and analysis when applied to other data. In this paper, we propose a novel short-term forecasting framework by tree-structured parzen estimator (TPE) and decomposition algorithms. This framework defines the TPE-VMD-TFT method for 24-h and 48-h ahead wind power forecasting based on variational mode decomposition (VMD) and time fusion transformer (TFT). In the Engie wind dataset from the electricity company in France, the results show that the proposed method significantly improves the prediction accuracy. In addition, the proposed framework can be used to other decomposition algorithms and require little manual work in model training

Collaborative adaptive filtering for machine learning

Quantitative performance criteria for the analysis of machine learning architectures and algorithms have long been established. However, qualitative performance criteria, which identify fundamental signal properties and ensure any processing preserves the desired properties, are still emerging. In many cases, whilst offline statistical tests exist such as assessment of nonlinearity or stochasticity, online tests which not only characterise but also track changes in the nature of the signal are lacking. To that end, by employing recent developments in signal characterisation, criteria are derived for the assessment of the changes in the nature of the processed signal. Through the fusion of the outputs of adaptive filters a single collaborative hybrid filter is produced. By tracking the dynamics of the mixing parameter of this filter, rather than the actual filter performance, a clear indication as to the current nature of the signal is given. Implementations of the proposed method show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. This is then extended (in the real domain) from dealing with nonlinearity in general, to a more specific example, namely sparsity. Extensions of adaptive filters from the real to the complex domain are non-trivial and the differences between the statistics in the real and complex domains need to be taken into account. In terms of signal characteristics, nonlinearity can be both split- and fully-complex and complex-valued data can be considered circular or noncircular. Furthermore, by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion. To produce online tests for sparsity, adaptive filters for sparse environments are investigated and a unifying framework for the derivation of proportionate normalised least mean square (PNLMS) algorithms is presented. This is then extended to derive variants with an adaptive step-size. In order to create an online test for noncircularity, a study of widely linear autoregressive modelling is presented, from which a proof of the convergence of the test for noncircularity can be given. Applications of this method are illustrated on examples such as biomedical signals, speech and wind data

Short-term load forecasting in times of unprecedented price movements

In this thesis we aimed to find the best methods for short-term load forecasting in the Norwegian electricity market during times of unprecedented price movements. We answered three questions related to this aim. The first was which model achieved the most accurate forecast. The second was whether our proposed models outperform the official forecasts published on the Entso-E platform. The third question asked was if the price movements had any effect on the accuracy of the load forecast. We constructed two SARIMAX models, a Gradient boosted decision tree, a Random Forest, and a Multilayer perceptron model. Our findings show the two SARIMAX models to be most accurate. These models outperformed the forecasts published on the Entso-E platform in four out of the five Norwegian bidding zones, measured in MAPE and RMSE. Finally, we have shown that forecasting load with and without price information did not result in significant differences in accuracy. Our findings did not indicate an increase in difficulty of forecasting 2021 compared to 2019, neither for the three southern bidding zones with higher price increase nor the northern two zones.I denne masteroppgaven har vi forsøkt å finne den beste metoden for kortsiktig prognostisering av elektrisitets-etterspørsel i perioder med ekstreme prisbevegelser. Vi har besvart tre spørsmål knyttet til denne problemstillingen. Det første var hvilken modell som oppnår høyest nøyaktighet. Det andre var om våre modeller presterer bedre enn de publiserte prognosene på Entso-Es offentlig tilgjengelige data-plattform. Det tredje spørsmålet var om de ekstreme prisbevegelsene hadde noen effekt på nøyaktigheten av prognosene. Vi har laget to SARIMAX modeller, en Gradient boosting decision tree-, en Random Forest og en Multilayer perceptron-modell. Gjennom arbeidet har vi vist at de to SARIMAX-modellene presterer best. Disse modellene er mer nøyaktig enn prognosene publisert på Entso-Es plattform for fire av de fem norske strømregionene, målt i MAPE og RMSE. Til slutt har vi vist at prognoser gjort både med og uten prisinformasjon ikke gir signifikante forskjeller i nøyaktighet. Det ble heller ikke påvist en klar forskjell i vanskelighetsgraden av å prognostisere 2021 sammenlignet med 2019, verken for de sørlige prissonene med høy prisvekst eller de nordlige sonene med en lavere prisvekst.M-Ø

On Independent Component Analysis and Supervised Dimension Reduction for Time Series

The main goal of this thesis work has been to develop tools to recover hidden structures, latent variables, or latent subspaces for multivariate and dependent time series data. The secondary goal has been to write computationally efficient algorithms for the methods to an R-package. In Blind Source Separation (BSS) the goal is to find uncorrelated latent sources by transforming the observed data in an appropriate way. In Independent Component Analysis (ICA) the latent sources are assumed to be independent. The well-known ICA methods FOBI and JADE are generalized to work with multivariate time series, where the latent components exhibit stochastic volatility. In such time series the volatility cannot be regarded as a constant in time, as often there are periods of high and periods of low volatility. The new methods are called gFOBI and gJADE. Also SOBI, a classic method which works well once the volatility is assumed to be constant, is given a variant called vSOBI, that also works with time series with stochastic volatility. In dimension reduction the idea is to transform the data into a new coordinate system, where the components are uncorrelated or even independent, and then keep only some of the transformed variables in such way that we do not lose too much of the important information of the data. The aforementioned BSS methods can be used in unsupervised dimension reduction; all the variables or time series have the same role. In supervised dimension reduction the relationship between a response and predictor variables needs to be considered as well. Wellknown supervised dimension reduction methods for independent and identically distributed data, SIR and SAVE, are generalized to work for time series data. The methods TSIR and TSAVE are introduced and shown to work well for time series, as they also use the information on the past values of the predictor time series. Also TSSH, a hybrid version of TSIR and TSAVE, is introduced. All the methods that have been developed in this thesis have also been implemented in R package tsBSS

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Joint optimisation of generation and storage in the presence of wind

As future grids are becoming more decentralised, I study a stand-alone grid where the penetration of wind energy is high, and exploit a joint planning of energy storage and renewable energy source, as this can potentially result in a more economical and efficient energy system. More specifically, I consider an energy system that consists of a gas-fired plant and a small wind farm with a capacity for energy storage. I assume that the gas-fired plant has a maximum generation that is no more than the electricity consumption. I first propose an optimisation model with known wind speed and electricity demand. Then I gradually extend this deterministic model to study the stochastic nature of the wind speed and electricity demand forecasting. Numerical applications in two chosen locations with different characteristics have been provided for demonstration. In the model extension, I compare battery storage with the other storage technologies by modifying the part of the cost functional, charging/discharging capacity and efficiency rate corresponding to the storage. The optimal solution has changed due to different efficiencies, costs and charging/discharging capacities. Compressed air energy storage and pumped hydroelectric storage may have the advantage in cost, but if a big surplus of energy is needed to get charged within a short time period, batteries might be a better choice as flywheels are very expensive. Furthermore, I include carbon emission modelling from the gas-fired plant by applying a carbon tax and a carbon emission cap. In my system, for a carbon tax to have a similar effect in reducing emissions in comparison to a carbon emission cap, it would need to be very high. Finally, I consider the possibility of connecting my system to the National Grid where I import from, or export to, when my system has an electricity shortage or surplus in meeting the demand. The results provide helpful insights in planning a joint deployment of generation capacity and energy storage and show that the system operates more efficiently and economically when it is connected to the National Grid