Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn

A Novel Integrated Profit Maximization Model for Retailers under Varied Penetration Levels of Photovoltaic Systems

In contemporary energy markets, the Retailer acts as the intermediate between the generation and demand sectors. The scope of the Retailer is to maximize its profits by selecting the appropriate procurement mechanism and selling price to the consumers. The wholesale market operation influences the profits since the mix of generation plants determines the system marginal price (SMP). In the related literature, the SMP is treated as a stochastic variable, and the wholesale market conditions are not taken into account. The present paper presents a novel methodology that aims at connecting the wholesale and retail market operations from a Retailer’s perspective. A wholesale market clearing problem is formulated and solved. The scope is to examine how different photovoltaics (PV) penetration levels in the generation side influences the profits of the Retailer and the selling prices to the consumers. The resulting SMPs are used as inputs in a retailer profit maximization problem. This approach allows the Retailer to minimize economic risks and maximize profits. The results indicate that different PV implementation levels on the generation side highly influences the profits and the selling prices

Formulation of typical load curves and application in load forecasting and demand response

Due to the continually changing scene in the planning and operation of electrical energy systems there is a need for the efficient treatment of many issues, for example the large increase of the demand, the optimal management of generation capacity, the effect of the power sector in the environment and others. Considerable part of the electrical energy systems related research recognizes the important role of the consumer in the new arising scene. Under this approach, the research community seeks for tools for the characterization of the energy patterns of the various consumer types. The clustering of the patterns is a first step for their characterization. The main scope of the present doctoral dissertation is the development of a model for the clustering of the energy pattern of various consumers. The clustering is formulated as an exclusively unsupervised machine learning problem. The patterns are distinguished based on the level of similarity they present, where external information about the optimal number of clusters is absent. A comparative analysis of all clustering algorithms that have been proposed in the literature takes place. High efficient algorithms that already used in other problems are proposed and new algorithms that improve the operation of existing ones are developed. The comparative analysis considers all the validity indicators of the algorithms. New indicators are examined, alternative forms of the existing indicators are recommended and new criteria for the algorithm comparisons are also recommended. Moreover, all the representation techniques of the literature via the load curves are expressed and examined and a new technique is recommended. Finally, methods for the reduction of the amount of the data are recommended. Next, the problem of the short-term bus load forecasting is treated through the context of the load curves clustering. The forecast refers to the extraction of the next day load curve or the next hour load. Models based on artificial neural networks are developed and hybrid models that combine neural networks and the clustering of loads. Additionally, the application of clustering in the formulation of dynamic tariffs is investigated. Dynamic pricing is a fundamental pillar of demand response. In the present dissertation the application of demand response measures is done through a retailer, where the objective is to maximize the profitability in a deregulated market environment. The retailer`s actions in the market are simulated via a model which simulate the demand response of the consumers to the dynamic tariffs, identifies the optimal procurement mechanism for serving the load and examines the influence of various factors in the profitability.Λόγω του συνεχώς μεταβαλλόμενου τοπίου στο σχεδιασμό και τη λειτουργία του Συστήματος Ηλεκτρικής Ενέργειας προκύπτει η ανάγκη για την αποδοτική διευθέτηση πολλών ζητημάτων, όπως η αύξηση της ζήτησης, η βέλτιστη διαχείριση του παραγωγικού δυναμικού, η επίδραση του τομέα της ηλεκτροπαραγωγής στο περιβάλλον και άλλα. Σημαντικό μέρος της έρευνας στα Συστήματα Ηλεκτρικής Ενέργειας επικεντρώνεται στο ρόλο του καταναλωτή στο νέο αναδυόμενο σκηνικό. Υπό αυτή τη θεώρηση, η ερευνητική κοινότητα αναζητάει εργαλεία για το χαρακτηρισμό των ενεργειακών προτύπων των διάφορων τύπων των καταναλωτών. Η συσταδοποίηση των προτύπων αποτελεί ένα πρώτο βήμα για το χαρακτηρισμό τους. Κύριος στόχος της παρούσας διατριβής αποτελεί η ανάπτυξη ενός μοντέλου συσταδοποίησης των ενεργειακών προτύπων διάφορων καταναλωτών. Η συσταδοποίηση διαμορφώνεται ως ένα πρόβλημα μη επιβλεπόμενης μηχανικής μάθησης. Τα πρότυπα διαχωρίζονται με βάση το βαθμό ομοιότητας που παρουσιάζουν, χωρίς προγενέστερη πληροφορία για το βέλτιστο αριθμό συστάδων. Υλοποιείται μια συστηματική διερεύνηση του συνόλου των αλγορίθμων συσταδοποίησης που έχουν προταθεί στη βιβλιογραφία. Προτείνονται αλγόριθμοι υψηλής απόδοσης που έχουν εφαρμοστεί σε άλλα προβλήματα και αναπτύσσονται νέοι αλγόριθμοι που βελτιώνουν τη λειτουργία υπαρχόντων. Η συγκριτική ανάλυση των αλγορίθμων περιλαμβάνει το σύνολο των δεικτών αξιολόγησης των αλγορίθμων. Εξετάζονται νέοι δείκτες, προτείνονται εναλλακτικές μορφές των υπαρχόντων και προτείνονται νέα κριτήρια για την ανάδειξη των αποδοτικότερων αλγορίθμων. Επιπλέον, εξετάζεται το σύνολο των τεχνικών αντιπροσώπευσης μέσω των οποίων εκφράζονται οι καμπύλες φορτίου και προτείνεται νέα τεχνική. Τέλος, προτείνονται μέθοδοι για τη μείωση της ποσότητας των δεδομένων. Στη συνέχεια, αντιμετωπίζεται ως ειδική εφαρμογή, το θέμα της βραχυπρόθεσμης πρόβλεψης φορτίων ζυγών με εφαρμογή της συσταδοποίησης των καμπυλών φορτίου. Η πρόβλεψη αναφέρεται στην εξαγωγή της καμπύλης της επόμενης ημέρας ή του φορτίου της επόμενης ώρας. Αναπτύσσονται μοντέλα πρόβλεψης που περιλαμβάνουν τεχνητά νευρωνικά δίκτυα και υβριδικά μοντέλα που συνδυάζουν τα νευρωνικά δίκτυα και τη διαδικασία της συσταδοποίησης των φορτίων. Επιπρόσθετα, γίνεται διερεύνηση της συσταδοποίησης στη διαμόρφωση δυναμικών τιμολογίων της ηλεκτρικής ενέργειας. Η δυναμική τιμολόγηση αποτελεί βασικό άξονα της απόκρισης της ζήτησης. Στη παρούσα διατριβή η εφαρμογή μέτρων απόκρισης της ζήτησης γίνεται από ένα προμηθευτή, με απώτερο στόχο τη μεγιστοποίηση της κερδοφορίας σε περιβάλλον ανταγωνιστικής αγοράς. Οι ενέργειες του προμηθευτή μέσα στην αγορά προσομοιώνονται μέσω ενός μοντέλου που εξομοιώνει την απόκριση της ζήτησης των καταναλωτών στα δυναμικά τιμολόγια, προσδιορίζει το βέλτιστο μηχανισμό κάλυψης του φορτίου και εξετάζει την επίδραση διάφορων παραγόντων στην κερδοφορία

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn

A data-driven short-term forecasting model for offshore wind speed prediction based on computational intelligence

This article belongs to the Special Issue Deep Learning Applications with Practical Measured Results in Electronics IndustriesWind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble” refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions

Implementation of pattern recognition algorithms in processing incomplete wind speed data for energy assessment of offshore wind turbines

Offshore wind turbine (OWT) installations are continually expanding as they are considered an efficient mechanism for covering a part of the energy consumption requirements. The assessment of the energy potential of OWTs for specific offshore sites is the key factor that defines their successful implementation, commercialization and sustainability. The data used for this assessment mainly refer to wind speed measurements. However, the data may not present homogeneity due to incomplete or missing entries; this in turn, is attributed to failures of the measuring devices or other factors. This fact may lead to considerable limitations in the OWTs energy potential assessment. This paper presents two novel methodologies to handle the problem of incomplete and missing data. Computational intelligence algorithms are utilized for the filling of the incomplete and missing data in order to build complete wind speed series. Finally, the complete wind speed series are used for assessing the energy potential of an OWT in a specific offshore site. In many real-world metering systems, due to meter failures, incomplete and missing data are frequently observed, leading to the need for robust data handling. The novelty of the paper can be summarized in the following points: (i) a comparison of clustering algorithms for extracting typical wind speed curves is presented for the OWT related literature and (ii) two efficient novel methods for missing and incomplete data are proposed

Clustering techniques for data analysis and data completion of monitored structural responses of an offshore floating structure

Offshore Floating Structures (OFSs) present a major category of offshore structures that are often subjected to severe environmental conditions and harsh critical loading scenarios. The state of an OFS during its life-cycle must remain in the domain specified in the design, although this can be altered by normal aging due to usage, the action of the environment and accidental events. In recent years, the field of Structural Health Monitoring (SHM) has been growing at a fast rate, especially in different applications within the offshore structures' field (e.g. platforms and systems in oil and gas technology, risers, and offshore wind technology). Based on the monitored data of the SHM a diagnosis and most importantly a prognosis of the health status of the OFS can be assessed. Usually, measured data in long time span of different structural response quantities are used for the aforementioned assessment with, in some cases, unmeasured data. This paper deals with two objectives for the case of monitored structural response data of an OFS: (i) the implementation of clustering techniques for analysis of the structural response data and (b) the completion of missing structural response data based on appropriate clustering techniques

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets

A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques

This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle