Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market

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

Implementación de algoritmos basados en máquinas de soporte vectorial (SVM) para sistemas eléctricos: revisión de tema

Objective: To perform a review of implementation of algorithms based on support vectore machine applied to electric systems.Method: A paper search is done mainly on Biblio­graphic Indexes (BI) and Bibliographic Bases with Selection Committee (BBSC) about support vector machine. This work shows a qualitative and/or quan­titative description about advances and applications in the electrical environment, approaching topics such as: electrical market prediction, demand predic­tion, non-technical losses (theft), alternative energy source and transformers, among others, in each work the respective citation is done in order to guarantee the copy right and allow to the reader a dynamic mo­vement between the reading and the cited works.Results: A detailed review is done, focused on the searching of implemented algorithms in electric sys­tems and innovating application areas.Conclusion: Support vector machines have a lot of applications due to their multiple benefits, however in the electric energy area; they have not been tota­lly applied, this allow to identify a promising area of researching.Objetivo: Realizar una revisión sobre la implementación de algoritmos basados en máquinas de soporte vectorial para sistemas eléctricos.Método: Se realiza una búsqueda de artículos principalmente en Índices bibliográficos (IB) y Bases Bibliográficas con Comité de Selección (BBCS) acerca de las máquinas de soporte vectorial. En este trabajo presenta una descripción cualitativa y/o cuantitativa acerca de los avances y aplicaciones en el entorno eléctrico, abordando temas como: predicción del mercado eléctrico, predicción de la demanda, perdidas no técnicas de electricidad (hurto), energías alternativas, trasformadores, entre otros, en cada trabajo se realiza la respectiva citación con el fin de garantizar los derechos de autor y permitirle al lector el movimiento dinámico entre lo consignado en este trabajo y los trabajos citados .Resultados: Se realiza la revisión de una manera detallada, centrando la búsqueda en algoritmos implementados en sistemas eléctricos y en área de aplicación novedosas.Conclusión: Las máquinas de soporte vectorial tiene bastantes aplicaciones debido a sus múltiples beneficios, sin embargo, en el área de energía eléctrica los campos de exploración no se han desarrollado en su totalidad, esto permite identificar un área prometedora de trabajos de investigación

Comparison of different models for forecasting of Czech electricity market

Mnoho rozdílných přístupů jako jsou umělé neuronové sítě nebo SVR bývá použito v literatuře. Tato práce poskytuje srovnání několika rozdílných metod v jednotných podmínkách za použití dat z Českého trhu s elektřinou. Výsledné srovnání více jak 5000 modelů vedlo k vybrání několika nejlepších modelů. Tato práce také vyhodnocuje roli historických meteorologických dat (teplota, rosný bod a vlhkost) - bylo zjištěno, že třebaže použití meteorologických může vést k přeučení, za vhodných podmínek může také vést k přesnějším modelům. Nejlepší testovaný přístup představovala Lasso regrese. 1There is a demand for decision support tools that can model the electricity markets and allows to forecast the hourly electricity price. Many different ap- proach such as artificial neural network or support vector regression are used in the literature. This thesis provides comparison of several different estima- tors under one settings using available data from Czech electricity market. The resulting comparison of over 5000 different estimators led to a selection of several best performing models. The role of historical weather data (temper- ature, dew point and humidity) is also assesed within the comparison and it was found that while the inclusion of weather data might lead to overfitting, it is beneficial under the right circumstances. The best performing approach was the Lasso regression estimated using modified Lars. 1Institut ekonomických studiíInstitute of Economic StudiesFaculty of Social SciencesFakulta sociálních vě

Short-term electricity price point and probabilistic forecasts

Accurate short-term electricity price forecasts are essential to all electricity market participants. Generation companies adopt price forecasts to hedge generation shortage risks; load serving entities use price forecasts to purchase energy with low cost; and trading companies utilize price forecasts to arbitrage between markets. Currently, researches on point forecast mainly focus on exploring periodic patterns of electricity price in time domain. However, frequency domain enables us to identify more information within price data to facilitate forecast. Besides, price spike forecast has not been fully studied in the existing works. Therefore, we propose a short-term electricity price forecast framework that analyzes price data in frequency domain and consider price spike predictions. First, the variational mode decomposition is adopted to decompose price data into multiple band-limited modes. Then, the extended discrete Fourier transform is used to transform the decomposed price mode into frequency domain and perform normal price forecasts. In addition, we utilize the enhanced structure preserving oversampling and synthetic minority oversampling technique to oversample price spike cases to improve price spike forecast accuracy. In addition to point forecasts, market participants also need probabilistic forecasts to quantify prediction uncertainties. However, there are several shortcomings within current researches. Although wide prediction intervals satisfy reliability requirement, the over-width intervals incur market participants to derive conservative decisions. Besides, although electricity price data follow heteroscedasticity distribution, to reduce computation burden, many researchers assume that price data follow normal distribution. Therefore, to handle the above-mentioned deficiencies, we propose an optimal prediction interval method. 1) By considering both reliability and sharpness, we ensure the prediction interval has a narrow width without sacrificing reliability. 2) To avoid distribution assumptions, we utilize the quantile regression to estimate the bounds of prediction intervals. 3) Exploiting the versatile abilities, the extreme learning machine method is adopted to forecast prediction intervals. The effectiveness of proposed point and probabilistic forecast methods are justified by using actual price data from various electricity markets. Comparing with the predictions derived from other researches, numerical results show that our methods could provide accurate and stable forecast results under different market situations

Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area