R Marriammal, Reni Hena Helen R, M Rubika, T Sowbhagya
Bitcoin, the king of cryptocurrencies, is central to blockchain technology. A fixed amount of bitcoins is required for each transaction stored in the blockchain. The price of bitcoins fluctuates wildly and is unaffected by any company or marketing techniques, creating both curiosity and terror in the minds of traders. It is possible for consumers to study and invest in bitcoin by anticipating the bitcoin price, which promotes the use of digital money. As a result, a high-prediction-rate prediction model is required. The goal of this project is to employ a variety of machine learning models to predict the price of bitcoin. The best model for predicting bitcoin value is given based on the error percentage of these machine learning algorithms.
Md Mahraj Murshalin Al Moti, Rafsan Shartaj Uddin, Md. Abdul Hai, Tanzim Bin Saleh · 7 authors
A smart grid is an intelligent electricity network that allows efficient electricity distribution from the source to consumers through telecommunication technology. The legacy smart grid follows the centralized oligopoly marketplace for electricity trading. This research proposes a blockchain-based electricity marketplace for the smart grid environment to introduce a decentralized ledger in the electricity market for enabling trust and traceability among the stakeholders. The electricity prices in the smart grid are dynamic in nature. Therefore, price forecasting in smart grids has paramount importance for the service providers to ensure service level agreement and also to maximize profit. This research introduced a Stackelberg model-based dynamic retail price forecasting of electricity in a smart grid. The Stackelberg model considered two-stage pricing between electricity producers to retailers and retailers to customers. To enable adaptive and dynamic price forecasting, reinforcement learning is used. Reinforcement learning provides an optimal price forecasting strategy through the online learning process. The use of blockchain will connect the service providers and consumers in a more secure transaction environment. It will help tackle the centralized system’s vulnerability by performing transactions through customers’ smart contracts. Thus, the integration of blockchain will not only make the smart grid system more secure, but also price forecasting with reinforcement learning will make it more optimized and scalable.
Xinchen Zhang, Linghao Zhang, Qincheng Zhou, Xu Jin
As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.
Zhiwei Chen, Wenxin Guo, Ruifeng Zhao, Yang Liu · 5 authors
The purpose is to realize the decentralized microgrid economic dispatch, improve the information transparency and security of microgrid systems, and make the power grid move towards a clean, safe, efficient, and reliable development path. Deep learning optimization of microgrid economic dispatch and wireless power transmission based on blockchain technology are studied. First, the related theories and methods of microgrid systems, wireless power transmission, and deep learning optimization based on blockchain technology are introduced. Next, the microgrid economic dispatch is simulated and analyzed on a large scale. Finally, the comparison results between microgrid economic dispatch and common radio energy transmission technologies are analyzed. The results show that daily planning can better coordinate the state of distributed generation, energy storage system, and public connection. The operation results of the previous day correspond to the long‐term operation economy of the microgrid. The total operation cost of the microgrid is 4668 yuan/day, and the remaining power is maintained between 500 and 600 kW, which helps to prevent excessive battery discharge, prolong battery life, and reduce operation cost. The simulation results show that the total power imbalance of the microgrid can reduce the output fluctuation of controllable load shedding of distributed generation. When the load characteristics are not important, the output fluctuation of controllable distributed generation can be reduced. The proposed economic dispatch model can optimize the data security, information storage, and information release of the microgrid and has a certain guiding role for the development of the national power grid and power industry.
The return series of cryptocurrencies, which are emerging digital assets, exhibit nonstationarity, nonlinearity, and volatility clustering compared to other traditional financial markets, making them exceptionally difficult to forecast. Therefore, accurate cryptocurrency price forecasting is important for both market participants and regulators. It has been demonstrated that improved data forecasting accuracy can be achieved through decomposition, but few researchers have performed information extraction on the residual series generated by data decomposition. Based on the construction of a "decomposition-optimization-integration" hybrid model framework, in this paper, we propose a multi-scale hybrid forecasting model that combines the residual components after primary decomposition for secondary decomposition and integration. This model uses the variational modal decomposition (VMD) method to decompose the original return series into a finite number of components and residual terms; then, the residual terms are decomposed and the features are extracted using the completed ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. The components are predicted by an extreme learning machine optimized by the sparrow search algorithm, and the final predictions are summed to obtain the final results. Forecasts for the returns of Bitcoin and Ethereum, which are major cryptocurrency assets, are compared with other benchmark models constructed based on different ideas, and we find that the proposed quadratic decomposition VMD-Res.-CEEMDAN-SSA-ELM hybrid model demonstrates the optimal and most stable forecasting performance in both one-step and multi-step ahead prediction of the cryptocurrency return series.
Aug 7, 2021·In: Awan, I., Benbernou, S., Younas, M., Aleksy, M. (eds) The International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021). Deep-BDB 2021. Lecture Notes in Networks and Systems, vol 309. Springer, Cham
On the Ethereum network, it is challenging to determine a gas price that ensures a transaction will be included in a block within a user's required timeline without overpaying. One way of addressing this problem is through the use of gas price oracles that utilize historical block data to recommend gas prices. However, when transaction volumes increase rapidly, these oracles often underestimate or overestimate the price. In this paper, we demonstrate how Gaussian process models can predict the distribution of the minimum price in an upcoming block when transaction volumes are increasing. This is effective because these processes account for time correlations between blocks. We performed an empirical analysis using the Gaussian process model on historical block data and compared the performance with GasStation-Express and Geth gas price oracles. The results suggest that when transactions volumes fluctuate greatly, the Gaussian process model offers a better estimation. Further, we demonstrated that GasStation-Express and Geth can be improved upon by using a smaller training sample size which is properly pre-processed. Based on the results of empirical analysis, we recommended a gas price oracle made up of a hybrid model consisting of both the Gaussian process and GasStation-Express. This oracle provides efficiency, accuracy, and better cost.
In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.
The interactive information in blockchain architecture establishes an effective communication channel between users and enterprises, enabling them to communicate in a comprehensive and effective manner. Therefore, taking blockchain interactive information as the research object, this paper explores how the intervention of official information on investors affects the stock price movement and then makes predictions on stock prices according to the emotional tendency of interactive information. With the contextual information fusion, a sentiment computing model based on a convolutional neural network is established to extract and quantify the emotional features of blockchain interactive information. Combined with investors’ emotional features, the stock price prediction model based on long short-term memory is proposed. The experiment results show that the accuracy of the model has been improved by incorporating the intervened emotional features, thereby proving that information clarification can have a positive effect on the stock price.
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.
This paper proposes and discusses the idea of using nascent blockchain hosted prediction markets as a decentralised crowd sourcing method for renewable energy forecasting. This method is further used as a risk management and hedging tool against volatility in weather variables they depend on. While existing approaches have been centralised by nature, with limited sources of input data and models, prediction markets allow anyone to participate in forecasting by betting on an outcome and earning profits for correct results. Since they have mercenary motivations, these participants are most likely to provide reliable and accurate information. Moreover, renewable energy producers can participate in these prediction markets to hedge against low-income periods due to poor weather conditions. This paper delivers a conceptual framework to exploit prediction markets in a blockchain platform with the aim of forecasting and hedging of renewable energy sources. The potential financial gain from applying this approach has been demonstrated through a case study for a typical small wind power producer.
In this study, aiming at the problem that the price of Bitcoin varies greatly and is difficult to predict, a hybrid neural network model based on convolutional neural network (CNN) and long short‐term memory (LSTM) neural network is proposed. The transaction data of Bitcoin itself, as well as external information, such as macroeconomic variables and investor attention, are taken as input. Firstly, CNN is used for feature extraction. Then the feature vectors are input into LSTM for training and forecasting the short‐term price of Bitcoin. The result shows that the CNN‐LSTM hybrid neural network can effectively improve the accuracy of value prediction and direction prediction compared with the single structure neural network. The finding has important implications for researchers and investors in the digital currencies market.
2008 yılında temelleri atılmış olan Kiripto para kavramı, 2017 yılı Aralık ayı itibari ile 19.060 ABD dolarına ulaşmış ve tanınırlığını arttırmıştır. Bitcoin ve sayıları 2700’ü bulan diğer kripto paralar hızlı kazanç elde etmek isteyen yatırımcıların dikkatini çekmeyi başarmıştır. Bu kapsamda kripto paraların fiyatının nasıl ve ne yönde değişeceği birçok kesim tarafından araştırma konusu olmuştur. Bu çalışmanın amacı, Bitcoin, Ethereum, IOTA ve Ripple gibi farklı altyapısal özellikleri olan kripto paraların gelecek fiyatını geçmişte gerçekleşen fiyatlardan hareketle tahmin etmektir. Çalışmada Deng Ju-Long tarafından 1980’li yıllarda ortaya atılan gri sistem teorisi ile fiyat tahminlemesi yapılmıştır. Çalışmada kullanılan geçmiş fiyatlar 11 günlük süreci kapsamaktadır. Literatüre göre kısa sayılabilecek bu süre modelin diğer modellere görece üstünlüğünü göstermektedir. Elde edilen sonuçlara göre GM(1,1) model ve Rolling-GM(1,1) model sonuçlarının birbirine çok yakın hata oranlarıyla tahmin yaptıkları ve yapılan tahminlere ait hata oranlarının çok düşük olduğu görülmüştür.
High frequency Bitcoin price series are often non-linear and non-stationary and hence forecasting the price of Bitcoin directly or by transformation using statistical models is subject to large errors. This paper presents an ensemble model using variational mode decomposition (VMD) and Generalized additive model (GAM) to forecast intraday Bitcoin price. To evaluate the performance of the constructed model, it is compared with an ensemble of empirical mode decomposition (EMD) and GAM. The results showed that VMD-GAM model performed better than the EMD-GAM ensemble model in terms of three evaluation metrics (root mean square error, mean absolute percentage error, and bias) used.
With the integration of highly permeable renewable energy to the grid at different levels (transmission, distribution and grid-connected), the volatility on both sides (source side and load side) leading to bidirectional power flow in the power grid complicates the control mechanism. In order to ensure the real-time power balance, energy exchange, higher energy utilization efficiency and stability maintenance in the electric power system, this paper proposes an integrated application of blockchain technology on energy routers at transmission and distribution networks with increased renewable energy penetration. This paper focuses on the safe and stable operation of a highly penetrated renewable energy grid-connected power system and its operation. It also demonstrates a blockchain-based negotiation model with weakly centralized scenarios for “source-network-load” collaborative scheduling operations; secondly, the QoS (quality of service) index of energy flow control and energy router node doubly-fed stability control model were designed. Further, it also introduces the MOPSO (multi-objective particle swarm optimization) algorithm for power output optimization of multienergy power generation; Thirdly, based on the blockchain underlying architecture and load prediction value constraints, this paper puts forward the optimization mechanism and control flow of autonomous energy coordination of b2u (bottom-up) between router nodes of transmission and distribution network based on blockchain.
In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. \n \nThis thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions.
Jan 1, 2020·Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia
In recent years, Bitcoin has attracted a lot of attention because of its nature that supports encryption technology and monetary units. For traders, Bitcoin becomes a promising investment since its fluctuating prices potentially draw high profit (the higher the risk the higher the return). Unlike co
The goal of this paper is to compare the accuracy of bitcoin price in USD prediction based on two different model, Long Short term Memory (LSTM) network and ARIMA model. Real-time price data is collected by Pycurl from Bitfine. LSTM model is implemented by Keras and TensorFlow. ARIMA model used in this paper is mainly to present a classical comparison of time series forecasting, as expected, it could make efficient prediction limited in short-time interval, and the outcome depends on the time period. The LSTM could reach a better performance, with extra, indispensable time for model training, especially via CPU.
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.
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.