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128 results · page 5 of 6
Uzma Amin, M. J. Hossain, Edstan Fernandez, Khizir Mahmud · 5 authors
The following topics are dealt with: power engineering computing; power grids; optimisation; distributed power generation; learning (artificial intelligence); power markets; neural nets; demand side management; evolutionary computation; power generation economics.
Nenad Petrović, Djordje Kocic
In this paper, we propose a framework for efficient energy trading. The main aspects of implementation are presented: a linear optimization model, load forecasting module based on deep learning approach and smart contract generator aiming the transaction execution supported by blockchain technology. Some evaluation elements are included: prediction performance, smart contract generation time and distribution cost reduction.
Chiao-Ting Chen, Lin-Kuan Chiang, Yi‐Cheng Huang, Szu-Hao Huang
Forecasting exchange rates is difficult because financial time-series data is too complicated to analyze. In traditional financial studies, economic models and statistic approaches were widely used for predicting exchange rates. Recently, machine learning and deep learning techniques have played increasingly important roles in financial technology studies. This study adopts a deep learning technique called relation networks (RNs) to predict the exchange rates of fiat currencies and cryptocurrencies. To discover the relationship among different currencies, the concept of visual question answering (VQA) is applied in RNs. We also propose a specially designed architecture for the feature extraction stage to consider both spatial and temporal relationships simultaneously. The experimental results show that the proposed approach can achieve higher prediction performance for cryptocurrencies with approximately 65% accuracy rate. We aim to improve traditional approaches and construct a model using the concept of VQA based on RNs to optimize the prediction performance between fiat currencies and cryptocurrencies.
Ümit Cali, Cláudio F. Lima
The main drivers of the third industrial revolution era were the internet technologies and rise of renewable and distributed energy technologies. Transition to green and decentralized energy resources and digital transformation of the existing industrial infrastructure had been the biggest achievements of the third industrial revolution. The main drivers of the fourth era will be artificial intelligence (AI), quantum computing, advanced biotechnology, internet of things, additive manufacturing, and most importantly, distributed ledger technology (DLT). Energy forecasting such as wind and solar power forecasting models are the most common energy AI-based informatics applications in the energy sector. In addition, use of DLT is expected to be an industrial standard in various industrial sectors including energy business in the coming decade. This chapter emphasizes description of energy forecasting using AI and energy DLT and future developments and solutions to overcome challenges that are associated with standardization of the energy DLT applications.
Ghulam Hafeez, Nadeem Javaid, M. Riaz, Ammar Ali · 6 authors
No abstract is available for this record.
Zixiao Xu, Dechang Yang, Weilin Li
With the development of the energy Internet and the integration of multi-type energy situations, it is of great significance to study the competition game of a multi-agent microgrid group system for its development. As an emerging distributed database technology, blockchain technology has great application potential in the field of energy trading. Firstly, blockchain technology is coupled with the microgrid group transaction, and the information flow transaction model of a microgrid group based on blockchain technology is established. Aiming at this complex multi-objective optimization problem, an improved ant colony optimization algorithm is proposed to solve the model. Finally, the competitive trading model and solving algorithm are simulated and analyzed. The relevant results show that the near global optimum price strategy of each time based on the proposed model can effectively balance the efficiency of each subject in the market. In addition, the model ensures that there is no high-income and low-cost phenomenon in the trading process, therefore the security and quality of the market are guaranteed.
Nor Azizah Hitam, Amelia Ritahani Ismail, Faisal Saeed
Forecasting accurate future price is very important in financial sector. An optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) is introduced in forecasting the cryptocurrency future price. It is part of Artificial Intelligence (AI) that uses previous experience to forecast future price. Analysts and investors generally combine fundamental and technical analysis prior to decide the best price to execute their trades. Some may use Machine Learning Algorithms to execute their trades. However, forecasting result using basic SVM algorithms does not really promising. On the other hands, Particle Swarm Optimization (PSO) is known as a better algorithm for a static and simple optimization problem. Therefore, PSO is introduced to optimize the algorithms of SVM in cryptocurrency forecasting. The experiment of selected cryptocurrencies is conducted for this classifier. The experimental result demonstrates that an optimized SVM-PSO algorithm can effectively forecast the future price of cryptocurrency thus outperforms the single SVM algorithms.
Fengji Luo, Zhao Yang Dong, Gaoqi Liang, Junichi Murata · 5 authors
The prevalence of distributed energy resources encourages the concept of an electricity “Prosumer (Producer and Consumer)”. This paper proposes a distributed electricity trading system to facilitate the peer-to-peer electricity sharing among prosumers. The proposed system includes two layers. In the first layer, a multi-agent system is designed to support the prosumer network, and an agent coalition mechanism is proposed to enable the prosumers to form coalitions and negotiate electricity trading. In the second layer, a Blockchain based transaction settlement mechanism is proposed to enable the trusted and secure settlement of electricity trading transactions formed in the first layer. Simulations are conducted based on the java agent development environment to validate the proposed electricity trading process.
Isil Yenidogan, Aykut Çayır, Ozan Kozan, Tugce Dag · 5 authors
This paper presents all studies, methodology, and results about Bitcoin forecasting with PROPHET and ARIMA methods using R analytics platform. To find the most accurate forecast model, the performance metrics of PROPHET and ARIMA methods are compared on the same dataset. The dataset selected for this study starts from May 2016 and ends in March 2018, which is the interval that Bitcoin values changing significantly against the other currencies. Data is prepared for time series analysis by performing data preprocessing steps such as time stamp conversion and feature selection. Although the time series analysis has a univariate characteristics, it is aimed to include some additional variables to each model to improve the forecasting accuracy. Those additional variables are selected based on different correlation studies between cryptocurrencies and real currencies. The model selection for both ARIMA and PROPHET is done by using threefold splitting technique considering the time series characteristics of the dataset. The threefold splitting technique gave the optimum ratios for training, validation, and test sets. Finally two different models are created and compared in terms of performance metrics. Based on the extensive testing we see that PROPHET outperforms ARIMA by 0.94 to 0.68 in R2values.
Hasan Alpago
Sanal para sistemi bitcoin ve altcoins olarak tanımlanan türevleri mevcut para politikasını ve para sistemlerini değişim ve dönüşüme zorlayacak bir trendin içinde oldukları gözlenmektedir. Genel olarak kripto para olarak tanımlanan bu sistem elektronik ortamda oluşturulabilen ve nakit benzeri bir ödeme aracı sisteminden ibarettir. Bu sistemin mevcut para ve ödeme araçlarına alternatif ve hatta geleneksel para teori ve uygulamalarının yerini alacağı yönünde bir gelişim süreci içinde olması bu sisteme odaklanmayı zorunlu hale getirmektedir. Bu makalede bitcoin ve benzeri kripto paraların yapıları, işlevleri ve mevcut para sistemi içerisindeki yeri ve önemi karşılaştırmalı ve analitik bir analizle değerlendirilmiştir.
Seçkin Karasu, Aytaç Altan, Zehra Saraç, Rıfat Hacıoğlu
In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012-2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training phase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model.
Sean Mcnally, Jason T. Roche, Simon Caton
The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.
M Vaidehi, Alivia Pandit, Bhaskar Jindal, Minu Kumari · 5 authors
After the boom and bust in cryptocurrencies’ prices in recent years, Bitcoin has been totally regarded as an investment asset. As it is highly volatile in nature, there has been a need for good predictions for carrying base investment decisions. Although current study has used machine learning for more accurate Bitcoin price prediction, some of them did focused on the feasibility of applying different modeling techniques to the samples that has different data structures and dimension features. To predict Bitcoin price on different frequencies after using machine learning techniques, firstly we have to classify the Bitcoin price with daily price and high-frequency price. Here, we attempt to predict Bitcoin price as accurately as possible by taking into consideration various protocols that affect the Bitcoin value. Using the provided data we would predict the sign of daily price change with highest possible accuracy. We have used Random Forest Classifier and compared with benchmark results as daily price prediction, we achieve a better performance, with the highest accuracies of the statistical methods and machine learning algorithms of 99%. my investigation in Bitcoin price prediction can be considered as a pilot study for the importance of the sample dimension in the machine learning techniques. Keywords Bitcoin, Crypto Currency, Machine Learning, Blockchain, Long Short Term Memory(LSTM), Recurrent Neural Network(RNN), Prediction
Petra Vrablecová, Anna Bou Ezzeddine, Viera Rozinajová, Slavomír Šárik · 5 authors
No abstract is available for this record.
Justin P. Jose, Vijaya Margaret, K. Uma Rao
In a Smart Grid environment the performance measure of the grid is calculated by considering the fact that how accurately and precisely a load forecasting (LF) is done. A true Load Forecasting is vital to make a current grid smarter and more reliable when it comes to its performance. Demand Response (DR) contracts is a type of program in smart grid where the customer is free to select a type of contract which is given by the utility and is one of the growing factor which affects the load forecasting results in the Smart Grid, therefore in order to do a complete evaluation of smart grid performance and to accomplish an accurate load forecasting results the different types of contracts should also be studied. The purpose of this study is to accomplish two goals. The first one is to develop a suitable model which can incorporate various factors that can affect the load forecasting results. The subsequent goal is to identify the impact of the demand response contracts on the load forecasting results. In the proposed study, Support Vector Machine-Regression (SVR) is selected as the base methodology to perform a Short — Term Load Forecasting (STLF) under smart grid environment.
Ashfaq Ahmad, Nadeem Javaid, Mohsen Guizani, Nabil Alrajeh · 5 authors
Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-to-day operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation. A large variety of STLF models have been developed that trade off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: mutual information based feature selection; and enhanced differential evolution algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network. Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing bilevel forecast strategy by 52.38%.
Kálmán Tornai, Lóránt Kovács, András Oláh, Rajmund Drenyovszki · 7 authors
No abstract is available for this record.
Bingtuan Gao, Cheng Wu, Yingjun Wu, Yi Tang
In the smart grid, large consumers can procure electricity energy from various power sources to meet their load demands. To maximize its profit, each large consumer needs to decide their energy procurement strategy under risks such as price fluctuations from the spot market and power quality issues. In this paper, an electric energy procurement decision-making model is studied for large consumers who can obtain their electric energy from the spot market, generation companies under bilateral contracts, the options market and self-production facilities in the smart grid. Considering the effect of unqualified electric energy, the profit model of large consumers is formulated. In order to measure the risks from the price fluctuations and power quality, the expected utility and entropy is employed. Consequently, the expected utility and entropy decision-making model is presented, which helps large consumers to minimize their expected profit of electricity procurement while properly limiting the volatility of this cost. Finally, a case study verifies the feasibility and effectiveness of the proposed model.
Hiroyuki Mori, Hajime Fujita
This paper proposes an efficient method for designing a contract model of the weather derivatives between energy utilities in Smart Grid. It is well-known that the weather conditions bring a profit decline or the increase of expenses to do damage to sound management. Weather derivatives are useful for solving such a problem. One of the ideas is to use the complementary relationship between electric power and gas companies in a sense that electric power companies are apt to make profits in hot summer while gas companies are inclined to reduce revenue. This paper focuses on how to create a reasonable contract model of the weather derivative. In this paper, EPSO (Evolutionary Particle Swarm Optimization) of meta-heuristics is applied to designing a contract model of the weather derivative. The proposed method aims at equalizing the mean and the variance of the payoffs between the power and gas companies. To enhance the model accuracy, DA clustering of global clustering is used to classify the historical data into clusters. The effectiveness of the proposed method is demonstrated for the real data in Tokyo, Japan.
Sérgio Ramos, João Duarte, João Soares, Zita Vale · 5 authors
The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer's behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.
Qun Zhou, Wei Guan, Wei Sun
Load forecasting is highly important for power system operation and planning. Demand response, as a valuable feature in smart grid, is growing dramatically as an effective demand management method. However, traditional load forecasting tools have limitations to reflect demand response customer behaviors into load predictions. The energy consumption by demand response customers is mostly guided by their signed contracts. Therefore, existing demand response contracts are reviewed in this study for both wholesale and retail markets. An illustrative example is provided to explore the impact of these contracts on load forecasting. A concept of proactive load forecasting considering contract types is then proposed and discussed for forecasting loads in a smart grid environment.