Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models are applied to historical data for two years: 2001 and 2002. The approaches are (1) to analyze nonlinear regression models for long term forecasting of annual patterns compared with outdoor temperature, and (2) to analyze multiple regression models for the building type regardless of outdoor temperature. In the first approach, twenty four buildings are categorized as base load group and no base group. Average temperature, cooling efficiencies, and cooling knot temperature are estimated by nonlinear regression models: segment and parabola models. The adjusted r-square results in good performance up to ninety percent accuracy. In the second approach, the other selected six buildings are categorized as no trend group. This group does not respond to outdoor temperature. As the result, multiple a regression model is formed by combination of variables from the nonlinear models and physical building variables of cooling efficiency, cooling temperature, light bulbs, area, outdoor temperature, and orientation of fronts. This model explains up to sixty percent of all convenience stores' data. In conclusion, the accuracy of prediction models is measured by the adjusted r-square results. Among these three models, the multiple regression model shows the highest adjusted r-square (0.597) over the parabola (0.5419) and segment models (0.4806). When the three models come to the application, the multiple regression model is best fit for no trend data type. However, when it is used to predict the energy consumption with the buildings that relate to outdoor temperature, segment and parabola model provide a better prediction result.

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In recent years, wind and other variable renewable energy sources have gained a rapidly increasing share of the global energy mix. In this context the greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian regularization is therefore seen as a possible technique for reducing model overfitting problems that may arise. This thesis investigates Bayesian Neural Networks in one-hour and day-ahead forecasting of wind power generation. Initial results show that Bayesian Neural Networks display equivalent predictive performance to Neural Networks trained by Maximum Likelihood in both one-hour and day ahead forecasting. Models selected using maximum evidence were found to have statistically significant lower test error performance compared to those selected based on minimum test error. Further results show that the Bayesian Framework is able to identify irrelevant features through Automatic Relevance Determination, though not resulting in a statistically significant error reduction in predictiveperformance in one-hour ahead forecasting. In day-ahead forecasting removing irrelevant features based on Automatic Relevance Determination is found to yield statistically significant improvements in test error. / Under de senaste åren har vind och andra variabla förnybara energikällor fått en snabbtökande andel av den globala energiandelen. I detta sammanhang är den största oron förförnybara energikällors produktionsvolymer vindosäkerheten, eftersom kraftverkens generationsförmåga i sig är beroende av väderförhållandena. Vid prognoser för nybyggdavindkraftverk finns en begränsad mängd historisk kraftproduktionsdata, medan antaletpotentiella mätvärden från olika väderprognosleverantörer är stor. Bayesian regulariseringses därför som en möjlig metod för att minska problem med den överanpassning avmodellerna som kan uppstå.Denna avhandling undersöker Bayesianska Neurala Nätverk (BNN) för prognosticeringen timme och en dag framåt av vindkraftproduktion. Resultat visar att BNN gerekvivalent prediktiv prestanda jämfört med neurala nätverk bildade med användandeav Maximum-likelihood för prognoser för en timme och dagsprognoser. Modeller somvalts med användning av maximum evidence visade sig ha statistiskt signifikant lägretestfelprestanda jämfört med de som valts utifrån minimaltestfel. Ytterligare resultatvisar att ett Bayesianskt ramverk kan identifiera irrelevanta särdrag genom automatiskrelevansbestämning. För prognoser för en timme framåt resulterar detta emellertid intei en statistiskt signifikant felreduktion i prediktiv prestanda. För 1-dagarsprognoser, närvi avlägsnar irrelevanta funktioner baserade på automatisk relevans, fås dock statistisktsignifikanta förbättringar av testfel.

Decentralization of the electric grid can increase resiliency (during natural disasters) and can reduce T&D energy losses and emissions. Microgrids and DERs can enable this to happen. It is important to optimally control microgrids and DERs to extract the greatest economic, environmental and resiliency benefits. This is enabled by robust forecasting to optimally control loads and energy sources. An integral part of microgrid control is power side and load side demand forecasting. In this thesis, we look at the ability of a powerful neural network algorithm to forecast the load side demand for a microgrid using the UMass campus as the test bed. UMass has its own power plant producing 16 MW of power. In addition to this, Solar panels totaling 5.5MW and lithium ion battery bank of 1.32 MW/4 MWh are also available. An LSTM recurrent neural network is used for demand forecasting. In addition to a fully trained LSTM network, multi linear regression model, ARIMA and ANN model are also tested to compare the performance. In addition to the Short Term Load Forecasting, the peak prediction accuracy of the model was also tested to run a battery discharge algorithm to shave peak demand for the microgrid. This will result in demand cost savings for the facility. Finally, the fully trained neural network was deployed on a raspberry pi computer.

In this thesis work, electricty generation modeling of the wind energy -one type of the renewable energy sources- is studied. The wind energy characteristics and the distribution of wind speed in a specific region is also examined. In addition, the power curves of the wind turbines are introduced and the relationship between the wind speed and wind power is explained. The generation characteristics of the wind turbines from various types of producers are also investigated. In this study, the main wind power forecasting methods are presented and the advantages and disadvantages of the methods are analyzed. The physical approaches, statistical methods and the Artificial Neural Network (ANN) methods are introduced. The parameters that affect the capacity factor, the total energy generation and the payback period are examined. In addition, the wind turbine models and their effect on the total energy generation with different wind data from various sites are explained. As a part of this study, a MATLAB-based software about wind speed and energy modelling and payback period calculation has been developed. In order to simplify the calculation process, a Graphical User Interface (GUI) has been designed. In addition, a simple wind energy persistence model for wind power plant operator in the intra-day market has been developed.

Elektrifieringen av bilflottan sker i rasande takt. Även andra samhällssektorers efterfrågan på el väntas öka drastiskt under kommande decennier, vilket i kombination med en växande andel intermittenta energikällor trappar upp påfrestningarna på elnätet och ställer krav på anpassningar. En föreslagen dellösning till kraftsystemets kommande utmaningar är att utnyttja efterfrågeflexibiliteten i laddande elbilar genom att en aggregator styr ett stort antal elbilsladdare och säljer den sammanlagda kapaciteten på till exempel Svenska kraftnäts stödtjänstmarknader.  För att avgöra hur mycket flexibilitet som elbilsladdning kan bidra med behöver aggregatorn upprätta prognoser över hur mycket effekt som mest sannolikt finns tillgänglig vid en viss tidpunkt – en punktprognos – men också en uppskattning av vilken effektnivå man kan vara nästan säker på att utfallet överstiger – en kvantilprognos. I den här studien har en undersökning gjorts av hur prognosfelet förändras om gruppen av aggregerade elbilsladdare ökas, och hur mycket en aggregator på så sätt kan sänka sina marginaler vid försäljning av efterfrågeflexibiliteten för att med säkerhet kunna uppfylla sitt bud. Det gjordes genom att kvantifiera flexibiliteten för 1 000 destinationsladdare belägna vid huvudsakligen arbetsplatser, och genom att skala upp och ner datamängden genom slumpmässiga urval. För dessa grupper gjordes sedan probabilistiska prognoser av flexibiliteten med en rullande medelvärdes- och en ARIMA-modell. Utifrån prognoserna simulerades slutligen potentiella intäkter om aggregatorn skulle använda den flexibla kapaciteten för uppreglering till stödtjänsten FCR-D upp, vilket är en frekvensreserv som aktiveras vid störningar av nätfrekvensen.  Resultaten visar att en tiodubbling av antalet aggregerade elbilsladdare mer än halverar det relativa prognosfelet. De båda prognosmodellerna visade sig ha jämförbar precision, vilket talar för att använda sig av den rullande medelvärdesmetoden på grund av dess lägre komplexitet. Den ökade säkerheten i prognosen resulterade dessutom i högre intäkter per laddare.  De genomsnittliga intäkterna av att leverera flexibilitet från 1 000 aggregerade elbilsladdare till FCR-D uppgick till 6 900 kr per månad, eller 0,8 kr per session – siffror som troligen hade varit högre utan coronapandemins ökade hemarbete. En 99-procentig konfidensgrad för kvantilprognosen resulterade i en säkerhetsmarginal med varierande storlek, som i genomsnitt var runt 90 procent för 100 laddpunkter, 60 procent för 1 000 laddpunkter samt 30 procent för 10 000 laddpunkter. Mest flexibilitet fanns tillgänglig under vardagsförmiddagar då ungefär 600 kW fanns tillgängligt som mest för 1 000 laddpunkter.  Att döma av tio års nätfrekvensdata är den sammanlagda sannolikheten för att över 50 procent aktivering av FCR-D-budet skulle sammanfalla med att utfallet för den tillgängliga kapaciteten är en-på-hundra-låg i princip obefintlig – en gång på drygt 511 år. Att aggregatorn lägger sina bud utifrån en 99-procentig konfidensgrad kan alltså anses säkert. / The electrification of the car fleet is taking place at a frenetic pace. Additionally, demand for electricity from other sectors of the Swedish society is expected to grow considerably in the coming decades, which in combination with an increasing proportion of intermittent energy sources puts increasing pressure on the electrical grid and prompts a need to adapt to these changes. A proposed solution to part of the power system's upcoming challenges is to utilize the flexibility available from charging electric vehicles (EVs) by letting an aggregator control a large number of EV chargers and sell the extra capacity to, for example, Svenska kraftnät's balancing markets. To quantify how much flexibility charging EVs can contribute with, the aggregator needs to make forecasts of how much power that is most likely available at a given time – a point forecast – but also an estimate of what power level the aggregator almost certainly will exceed – a quantile forecast. In this study, an investigation has been made of how the forecast error changes if the amount of aggregated EV chargers is increased, and how much an aggregator can lower their margins when selling the flexibility to be able to deliver according to the bid with certainty. This was done by quantifying the flexibility of 1000 EV chargers located at mainly workplaces, and by scaling up and down the data through random sampling. For these groups, probabilistic forecasts of the flexibility were then made with a moving average forecast as well as an ARIMA model. Based on the forecasts, potential revenues were finally simulated for the case where the aggregator uses the available flexibility for up-regulation to the balancing market FCR-D up, which is a frequency containment reserve that is activated in the event of disturbances. The results show that a tenfold increase in the number of aggregated EV chargers more than halves the forecast error. The two forecast models proved to have comparable precision, which suggests that the moving average forecast is recommended due to its lower complexity compared to the ARIMA model. The increased precision in the forecasts also resulted in higher revenues per charger. The average income from delivering flexibility from 1000 aggregated electric car chargers to FCR-D amounted to SEK 6900 per month, or SEK 0.8 per session – figures that would probably have been higher without the corona pandemic's increased share of work done from home. A 99 percent confidence level for the quantile forecast resulted in a safety margin of varying size, which on average was around 90 percent for 100 chargers, 60 percent for 1000 chargers and 30 percent for 10,000 chargers. Most flexibility was shown to be available on weekday mornings when approximately 600 kW was available at most for 1000 chargers. By examining frequency data for the Nordic power grid from the past ten years, the joint probability that a more than 50 percent activation of the FCR-D bid would coincide with the outcome for the available capacity being one-in-a-hundred-low, was concluded to be nearly non-existent – likely only once in about 511 years. For the aggregator to place bids based on a 99 percent confidence level can thus be considered safe.

With the growing installation of home photovoltaics, traditional energy trading is evolving from a unidirectional utility-to-consumer model into a more distributed peer-to-peer paradigm. Besides, with the development of building energy management platforms and demand response-enabled smart devices, energy consumption saved, known as negawatt-hours, has also emerged as another commodity that can be exchanged. Users may tune their heating, ventilation, and air conditioning (HVAC) system setpoints to adjust building hourly energy consumption to generate negawatt-hours. Both photovoltaic (PV) energy and negawatt-hours are two major resources of peer-to-peer electricity trading. Blockchain has been touted as an enabler for trustworthy and reliable peer-to-peer trading to facilitate the deployment of such distributed electricity trading through encrypted processes and records. Unfortunately, blockchain cannot fully detect anomalous participant behaviors or malicious inputs to the network. Consequentially, end-user anomaly detection is imperative in enhancing trust in peer-to-peer electricity trading. This dissertation introduces machine learning-based anomaly detection techniques in peer-to-peer PV energy and negawatt-hour trading. This can help predict the next hour's PV energy and negawatt-hours available and flag potential anomalies when submitted bids. As the traditional energy trading market is agnostic to tangible real-world resources, developing, evaluating, and integrating machine learning forecasting-based anomaly detection methods can give users knowledge of reasonable bid offer quantity. Suppose a user intentionally or unintentionally submits extremely high/low bids that do not match their solar panel capability or are not backed by substantial negawatt-hours and PV energy resources. Some anomalies occur because the participant's sensor is suffering from integrity errors. At the same time, some other abnormal offers are maliciously submitted intentionally to benefit attackers themselves from market disruption. In both cases, anomalies should be detected by the algorithm and rejected by the market. Artificial Neural Networks (ANN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and Convolutional Neural Network (CNN) are compared and studied in PV energy and negawatt-hour forecasting. The semi-supervised anomaly detection framework is explained, and its performance is demonstrated. The threshold values of anomaly detection are determined based on the model trained on historical data. Besides ambient weather information, HVAC setpoint and building occupancy are input parameters to predict building hourly energy consumption in negawatt-hour trading. The building model is trained and managed by negawatt-hour aggregators. CO2 monitoring devices are integrated into the cloud-based smart building platform BEMOSS™ to demonstrate occupancy levels, further improving building load forecasting accuracy in negawatt-hour trading. The relationship between building occupancy and CO2 measurement is analyzed. Finally, experiments based on the Hyperledger platform demonstrate blockchain-based peer-to-peer energy trading and how the platform detects anomalies. / Doctor of Philosophy / The modern power grid is transforming from unidirectional to transactive power systems. Distributed peer-to-peer (P2P) energy trading is becoming more and more popular. Rooftop PV energy and negawatt-hours as two main sources of electricity assets are playing important roles in peer-to-peer energy trading. It enables the building owner to join the electricity market as both energy consumer and producer, also named prosumer. While P2P energy trading participants are usually un-informed and do not know how much energy they can generate during the next hour. Thus, a system is needed to guide the participant to submit a reasonable amount of PV energy or negawatt-hours to be supplied. This dissertation develops a machine learning-based anomaly detection model for an energy trading platform to detect the reasonable PV energy and negawatt-hours available for the next hour's electricity trading market. The anomaly detection performance of this framework is analyzed. The building load forecasting model used in negawatt-hour trading also considers the effect of building occupancy level and HVAC setpoint adjustment. Moreover, the implication of CO2 measurement devices to monitor building occupancy levels is demonstrated. Finally, a simple Hyperledger-based electricity trading platform that enables participants to sell photovoltaic solar energy/ negawatt-hours to other participants is simulated to demonstrate the potential benefits of blockchain.

Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy.

Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy.

The ability of generating precise numerical forecasts is important to successful Enterprises in order to prepare themselves for undetermined future developments. For Utility companies, forecasts of prospective energy demand are a crucial component in order to maintain the physical stability and reliability of electricity grids. The constantly increasing capacity of fluctuating renewable energy sources creates a challenge in balancing power supply and demand. To allow for better integration, supply forecasting has become an important topic in the research field of energy data management and many new forecasting methods have been proposed in the literature. However, choosing the optimal solution for a specific forecasting problem remains a time- and work-intensive Task as meaningful benchmarks are rare and there is still no standard, easy-to-use, and robust approach. Many of the models in use are obtained by executing black-box machine learning tools and then manually optimized by human experts via trial-and-error towards the requirements of the underlying use case. Due to the lack of standardized Evaluation methodologies and access to experimental data, these results are not easily comparable. In this thesis, we address the topic of systematic benchmarks for renewable Energy supply forecasts. These usually include two stages, requiring a weather- and an energy forecast model. The latter can be selected amongst the classes of physical, statistical, and hybrid models. The selection of an appropriate model is one of the major tasks included in the forecasting process. We conducted an empirical analysis to assess the most popular forecasting methods. In contrast to the classical time- and resource intensive, mostly manual evaluation procedure, we developed a more efficient decision-support solution. With the inclusion of contextual information, our heuristic approach HMR is able to identify suitable examples in a case base and generates a recommendation out of the results from already existing solutions. The usage of time series representations reduces the dimensions of the original data thus allowing for an efficient search in large data sets. A context-aware evaluation methodology is introduced to assess a forecast’s quality based on its monetary return in the corresponding market environment. Results otherwise usually evaluated using statistical accuracy criteria become more interpretable by estimating real-world impacts. Finally, we introduced the ECAST framework as an open and easy to-use online platform that supports the benchmarking of energy time series forecasting methods. It aides inexperienced practitioners by supporting the execution of automated tasks, thus making complex benchmarks much more efficient and easy to handle. The integration of modules like the Ensembler, the Recommender, and the Evaluator provide additional value for forecasters. Reliable benchmarks can be conducted on this basis, while analytical functions for output explanation provide transparency for the user.:1 INTRODUCTION 11 2 ENERGY DATA MANAGEMENT CHALLENGES 17 2.1 Market Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2 EDMS Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.1 Core Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.2 Typical Energy Data Management Processes . . . . . . . . . . . 23 2.2.3 System Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.1 Smart Metering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.2 Energy Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.3.3 Energy Saving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.4 Mobile Consumption Devices . . . . . . . . . . . . . . . . . . . . . 30 2.3.5 Smart Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 ENERGY SUPPLY FORECASTING CONCEPTS 35 3.1 Energy Supply Forecasting Approaches . . . . . . . . . . . . . . . . . . . 36 3.1.1 Weather Forecast Models . . . . . . . . . . . . . . . . . . . . . . . . 36 3.1.2 Energy Forecast Models . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Energy Forecasting Process . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.2.1 Iterative Standard Process Model . . . . . . . . . . . . . . . . . . . 43 3.2.2 Context-Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3 Model Selection - A Benchmark Case Study . . . . . . . . . . . . . . . . 48 3.3.1 Use Case Description . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3.3 Result Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4 RELEVANCE OF RENEWABLE ENERGY FORECASTING METHODS 55 4.1 Scientific Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.1.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.1.2 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.1.3 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2 Practical Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.2 Feedback from Software Providers . . . . . . . . . . . . . . . . . . 61 4.2.3 Feedback from Software Users . . . . . . . . . . . . . . . . . . . . . 62 4.3 Forecasting Competitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5 HEURISTIC MODEL RECOMMENDATION 67 5.1 Property-based Similarity Determination . . . . . . . . . . . . . . . . . . 67 5.1.1 Time Series Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.1.2 Reducing Dimensionality with Property Extraction . . . . . . . . . 69 5.1.3 Correlation Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.2 Feature Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.2.1 Feature Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.2.2 Feature Pre-Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.2.3 Property-based Least Angle Regression . . . . . . . . . . . . . . . 85 5.3 HMR Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.3.1 Formalized Foundations . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.3.2 Procedure Description . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.3.3 Quality Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.4.1 Case Base Composition . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.4.2 Classifier Performance on univariate Models . . . . . . . . . . . . 95 5.4.3 HMR performance on multivariate models . . . . . . . . . . . . . 99 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 6 VALUE-BASED RESULT EVALUATION METHODOLOGY 105 6.1 Accuracy evaluation in energy forecasting . . . . . . . . . . . . . . . . 106 6.2 Energy market models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.3 Value-based forecasting performance . . . . . . . . . . . . . . . . . . . 110 6.3.1 Forecast Benefit Determination . . . . . . . . . . . . . . . . . . . . 110 6.3.2 Multi-dimensional Ranking Scores . . . . . . . . . . . . . . . . . . . 113 6.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.4.1 Use Case Description . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6.4.3 Result Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 7 ECAST BENCHMARK FRAMEWORK 129 7.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7.1.1 Objective Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7.1.2 Fundamental Design Principles . . . . . . . . . . . . . . . . . . . . 131 7.2 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 7.2.1 Task Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 7.2.2 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 7.3 Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 7.3.1 Step 1: Create a new Benchmark . . . . . . . . . . . . . . . . . . 137 7.3.2 Step 2: Build Ensembles . . . . . . . . . . . . . . . . . . . . . . . . . 139 7.3.3 Step 3: Evaluate the Output . . . . . . . . . . . . . . . . . . . . . . 141 7.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 8 CONCLUSIONS 145 BIBLIOGRAPHY 149 LIST OF FIGURES 167 LIST OF TABLES 169 A LIST OF REVIEWED JOURNAL ARTICLES 171 B QUESTIONNAIRES 175 C STANDARD ERRORS FOR RANKING SCORES 179 D ERROR DISTRIBUTION FOR BENCHMARKED PREDICTORS 183