Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Challenging the Efficient Market Hypothesis with Dynamically Trained Artificial Neural Networks

A review of the literature applying Multilayer Perceptron (MLP) based Artificial Neural Networks (ANNs) to market forecasting leads to three observations: 1) It is clear that simple ANNs, like other nonlinear machine learning techniques, are capable of approximating general market trends 2) It is not clear to what extent such forecasted trends are reliably exploitable in terms of profits obtained via trading activity 3) Most research with ANNs reporting profitable trading activity relies on ANN models trained over one fixed interval which is then tested on a separate out-of-sample fixed interval, and it is not clear to what extent these results may generalize to other out-of-sample periods. Very little research has tested the profitability of ANN models over multiple out-of-sample periods, and the author knows of no pure ANN (non-hybrid) systems that do so while being dynamically retrained on new data. This thesis tests the capacity of MLP type ANNs to reliably generate profitable trading signals over rolling training and testing periods. Traditional error statistics serve as descriptive rather than performance measures in this research, as they are of limited use for assessing a system’s ability to consistently produce above-market returns. Performance is measured for the ANN system by the average returns accumulated over multiple runs over multiple periods, and these averages are compared with the traditional buy-and-hold returns for the same periods. In some cases, our models were able to produce above-market returns over many years. These returns, however, proved to be highly sensitive to variability in the training, validation and testing datasets as well as to the market dynamics at play during initial deployment. We argue that credible challenges to the Efficient Market Hypothesis (EMH) by machine learning techniques must demonstrate that returns produced by their models are not similarly susceptible to such variability

Deep learning approaches for MIMO time-series analysis

This study presents a comparative analysis of various deep learning (DL) methods for multi-input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is conducted on a dataset comprising the stock price of Bitcoin. The dataset consists of 2950 rows from December 2017 to December 2021. This study aims to evaluate the performance of multiple DL methods, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). The evaluation criteria for selecting the best-performing methods in this research are based on two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These metrics were chosen for specific reasons related to assessing the accuracy and reliability of the forecasting models. MAPE is used to assess accuracy, while RMSE helps detect outliers in the system. Results show that the LSTM method achieves the best performance, outperforming other methods with an average MAPE value of 8.73% and Bi-LSTM has the best average RMSE value of 0.02216. The findings of this study have practical implications for time-series forecasting in the field of stock trading. The superior performance of LSTM highlights its potential as a reliable method for accurately predicting stock prices. The Bi-LSTM model's ability to detect outliers can aid in identifying abnormal stock market behavior. In summary, this research provides insights into the performance of various DL models of MIMO for stock price forecasting. The results contribute to the field of time-series forecasting and offer valuable guidance for decision-making in stock trading by identifying the most effective methods for predicting stock prices accurately and detecting unusual market behavior

Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Predictive maintenance in hydropower plants : a case study of valves and servomotors

Digitalization has opened the opportunity for a fourth industrial revolution and the hydropower industry is taking charge of enabling digitalization in their operation. There are a lot of studies on predictive maintenance, however, there are, to our knowledge no studies on system-specific predictive maintenance for hydropower. To bridge this gap, the idea of system-specific, Machine Learning driven Predictive Maintenance is explored. Two systems are chosen as a use-case for this thesis: valves and servomotors. With the increasing amount of intermittent renewable energy resources entering the power system, the need for flexibility in the power grid is unequivocal. Valves and servomotors are key components of hydropower control and thus will play a pivotal role in securing flexibility to the grid. The first system assessed is the main valve. In order to make this analysis easily applicable, the data that is already being collected at Nore 1 hydropower plant is analyzed in order to assess the possibility of maintenance prediction from limited data. Unfortunately, this did not achieve the desired results for the data collected from the valve sensors. This is due to the fact that only one variable was measured, in this case, the opening and closing time-lag of the valve. However, this thesis presents a framework for data collection that allows the use of Machine Learning for predictive maintenance. Various sensors are suggested based on several published works on predictive maintenance. The second system assessed is the servomotor that controls the guiding vanes in a Francis turbine. Servomotors are key components of hydropower control. Due to the data not being collected by Statkraft at the time of the study, this data was provided by one of Statkrafts suppliers. By making use of the historical data of pressure as a function of the piston position, a boundary for where new values should be expected is computed by making use of One Class Support Vector Machine. Another embodiment of this case is presented where force is given as a function of piston position, which yielded better results. When new values are being measured, the data is presented as a bullet chart that visualizes the distance of new values compared to the boundary computed by the One Class Support Vector Machine. This tool could easily be applied to other servomotors which perform other tasks such as controlling water injection to a Pelton turbine or opening and closing of the valve, whether they are butterfly or ball valves. Suggestions for further data collection are presented in order to make use of more data for the use of Machine Learning in Predictive Maintenance.Digitalisering har ledet frem til en fjerde industriell revolusjon og vannkraft bransjen er i ferd med å digitalisere sin operasjon. Under literaturstudien er det ikke funnet noen publiseringer innen systemspesifikk maskinlæringsdrevet predikativ vedlikehold. I denne masteroppgaven blir muligheten for bruk av systemspesifikk, maskinlæringdrevet predikativ vedlikehold innen vannkraftverk utforsket for å vekke interesse innen dette feltet. To av vannkraftverkenes maskiner er brukt som eksempler og utforsket: ventiler og servomotor. Økende mengder uregulerbar strøm er introdusert i kraftnettet og behovet for fleksibilitet øker. Ventiler og servomotor er nøkkeldeler av vannkraftverk regulering og spiller en stor rolle i å sikre flexibilitet til strømnettet. Det første systemet som ble analysert er ventiler. For å gjøre analysen og resultatene enkelt anvendbare, blir data som allerede er innsamlet analysert for å utforske muligheten for predikativ vedlikehold med begrenset data. Analysene basert på data samlet inn fra sensorne montert på ventilene ble dessverre ikke konklusive. Det er utfordrende å forutsi fremtiden når man bare har en variabel å ta utgangspunkt i. Likevel presenteres det et prinsipielt rammeverk for innsamling av data som gjør det mulig å ta i bruk maskinlæring for predikativ vedlikehold. Ulike sensorer er foreslått, basert på relevant litteratur innen ventiler og maskinlæring drevet predikativ vedlikehold. Det andre systemet analysert under studien er servomotorer som styrer vannet i en Francis turbin ved å regulere vinklingen til skovlene. Dataen innsamlet om servomotoren er en god indikator på tilstanden til servomotoren. Ettersom dataen var ikke samlet inn av Statkraft da studien ble utført, ble dataen hentet fra en av Statkraft sine leverandører. En One Class Support Vector Machine ble brukt for å beregne foventet verdi av differansetrykk over stempelkamrene, som funksjon av stempel posisjon. En kulegraf som viser avstanden mellom grensen og nye verdier er visualisert. En annen metode er også presentert hvor man regner ut kraft på begge sider av stempelkamrene gjennom trykk for å vise kraft som funksjon av stempel posisjon. Dette ga bedre valideringsresultater i forventet differansekraft over tempelkamrene. Verktøyet kan enkelt bli anvendt til andre servomotorer som styrer vannmengden i en Pelton turbin eller åpning og lukking av ventilene, uavhengig av om det er spjeld- eller kuleventiler. Forslag til videre data innsamling er presentert for å ta i bruk maskinlæring for predikativ vedlikehold.StatkraftsubmittedVersionM-M

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

A State-of-the-Art Review of Time Series Forecasting Using Deep Learning Approaches

Time series forecasting has recently emerged as a crucial study area with a wide spectrum of real-world applications. The complexity of data processing originates from the amount of data processed in the digital world. Despite a long history of successful time-series research using classic statistical methodologies, there are some limits in dealing with an enormous amount of data and non-linearity. Deep learning techniques effectually handle the complicated nature of time series data. The effective analysis of deep learning approaches like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Autoencoders, and other techniques like attention mechanism, transfer learning, and dimensionality reduction are discussed with their merits and limitations. The performance evaluation metrics used to validate the model's accuracy are discussed. This paper reviews various time series applications using deep learning approaches with their benefits, challenges, and opportunities