The accurate prediction of an uncertain future is crucial for society. Particularly when it comes to finance, where it holds vital importance for companies. Time series analysis is conducted to forecast the potential value of financial assets in the near future. Due to the impracticality of manually analyzing hundreds of indicators, machine learning methods are frequently employed. This study showcases the optimal value of the lag hyperparameter used in Random Forest and Extreme Gradient Boosting methods. The daily Bitcoin OHLCV data of a time series was enriched with indicators, and it was observed that the lag hyperparameter yielded the best results when considering the preceding 23 time series units.
Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they properly estimate supply and demand variations and optimize energy distribution, and the other featured values, for participants in decentralized energy marketplaces. Consequently, DeepResTrade is a research work that presents an advanced model for predicting prices in a given traditional energy market. This model includes numerous fundamental components, including the concept of P2P trading systems, long-term and short-term memory (LSTM) networks, decision trees (DT), and Blockchain. DeepResTrade utilized a dataset with 70,084 data points, which included maximum/minimum capacities, as well as renewable generation, and price utilized of the communities. The developed model obtains a significant predictive performance of 0.000636% Mean Absolute Percentage Error (MAPE) and 0.000975% Root Mean Square Percentage Error (RMSPE). DeepResTrade’s performance is demonstrated by its RMSE of 0.016079 and MAE of 0.009125, indicating its capacity to reduce the difference between anticipated and actual prices. The model performs admirably in describing actual price variations in, as shown by a considerable R2 score of 0.999998. Furthermore, F1/recall scores of [1, 1, 1] with a precision of 1, all imply its accuracy.
With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hot spot in the power industry. This paper aims to combine neural network algorithms with blockchain technology to establish a trustworthy and efficient short-term demand forecasting model. By leveraging the distributed ledger and immutability features of blockchain, we ensure the security and reliability of power demand data. Meanwhile, short-term power demand forecasting research using neural networks has the potential to increase the stability of the power system and offer opportunities for improved operations. In this paper, the root mean-square-error model evaluation indicator was used to compare the back propagation (BP) neural network algorithm and the traditional forecasting algorithm. The evaluation was performed on the randomly selected five household power datasets. The results show that, by comparing the long short-term memory network (LSTM) model with the BP neural network model, it was determined that the average prediction impact increases by about 25.7% under stable power demand. The short-term power prediction model of the BP neural network has the average error values more than two times lower than the traditional prediction model. It was shown that the use of the BP neural network algorithm and blockchain could increase the accuracy of short-term power demand forecasting, allowing the neural network-based algorithm to be implemented and taken into account in the research on short-term power demand forecasting.
Mahfuzur Rahman, Solaiman Chowdhury, Mohammad Shorfuzzaman, Mohammad Kamal Hossain · 5 authors
The advancement of mircogrids and the adoption of blockchain technology in the energy-trading sector can build a robust and sustainable energy infrastructure. The decentralization and transparency of blockchain technology have several advantages for data management, security, and trust. In particular, the uses of smart contracts can provide automated transaction in energy trading. Individual entities (household, industries, institutes, etc.) have shown increasing interest in producing power from potential renewable energy sources for their own usage and also in distributing this power to the energy market if possible. The key success in energy trading significantly depends on understanding one’s own energy demand and production capability. For example, the production from a solar panel is highly correlated with the weather condition, and an efficient machine learning model can characterize the relationship to estimate the production at any time. In this article, we propose an architecture for energy trading that uses smart contracts in conjunction with an efficient machine learning algorithm to determine participants’ appropriate energy productions and streamline the auction process. We conducted an analysis on various machine learning models to identify the best suited model to be used with the smart contract in energy trading.
This article reveals a specific category of solutions for the 1+1 variable order (VO) nonlinear fractional Fokker-Planck equations. These solutions are formulated using VO q-Gaussian functions, granting them significant versatility in their application to various real-world systems, such as financial economy areas spanning from conventional stock markets to cryptocurrencies. The VO q-Gaussian functions provide a more robust expression for the distribution function of price returns in real-world systems. Additionally, we analyzed the temporal evolution of the anomalous characteristic exponents derived from our study, which are associated with the long-term (power-law) memory in time series data and autocorrelation patterns.
I Made Gede Abandi Semeru, Yunieta Anny Nainggolan
Abstract : In forming their portfolios, investors should analyze the risk and return of each investment instrument. This is aimed at preventing investors from speculating and gambling with their investments. Conducting an investment portfolio optimization study on LQ-45 stock index, government bond, USD, gold, and Bitcoin can provide valuable insights due to unique market characteristics in Indonesia. This research analyzes the formation of investment instruments over the last 60 months, specifically from January 2018 to December 2022. The research method used in this study is quantitative research aimed at selecting several investment instruments for a portfolio in Indonesia. The portfolio aims to minimize risk and maximize return using the Markowitz method, also known as the optimal portfolio. To fulfill the objectives of this research, data on the prices of each instrument are required. An optimal portfolio can be obtained by combining two instruments: 18% bitcoin and 82% gold. This optimal portfolio can achieve an expected return of 1.29% with a risk level of 5.15%. Considering a risk-free rate of 0.375%, this portfolio forms a slope of 0.1775, which is the largest slope formed between the combination of risk-free instruments and risky portfolios. Investors should allocate their funds more wisely, considering not only the highest return but also the associated risk. High returns often come with high risks, so investors need to assess the risk-return trade-off before making investment decisions.
The term “energy prosumer” refers to a new job category that has emerged with the advent of distributed energy production via residential and commercial photovoltaic (PV) applications. The conventional distinction between energy providers and consumers becomes muddled as a result. For the procedure to be automated, blockchain technology has been used. This will make it easier for consumers, prosumers, and utilities to deal directly with energy safely, conveniently, and cost-effectively. This research aims to provide an agent-based modeling (ABM) simulation framework for energy exchange, exhibiting the anticipated power profiles of families and illustrating the operation of blockchain operations (see Figure. 1). A distributed energy resource (DER) of the transactive energy (TE) type was simulated inside the Education City Community Housing (ECCH) microgrid using a reliable multi-agent framework. Blockchain technology is necessary for this. Current blockchain-based local energy market (LEM) plans depend on precise short-term energy generation forecasts and home consumption to balance supply and demand. The present research evaluated the precision of cutting-edge energy forecasting methods in estimating homes’ energy production and consumption. It examined the effects of forecasting mistakes on market results under various supply scenarios. While LSTM models may provide few forecasting errors, the researchers discovered that the prediction process must be changed for a LEM constructed on a blockchain. Since this study tries to predict the timeline of smart meters generally, it sets itself apart from past investigations.
The long short-term memory (LSTM) network and a cutting-edge method that combines wavelet decomposition and LSTM (W-LSTM) were applied to deep learning in this study's analysis of Bitcoin's price and movement. To be specific, it predicted next day’s both price and price movement (trend) with historical data. The input of the model is close price itself, basic trading information, and technical indicators calculated solely on basic trading information. Large number of numerical experiments come to the same conclusion that: for price prediction, only close price as input obtains the best performance for regression, and minor improvement achieved after 1-order wavelet decomposition; for price movement, no improvement after changing the number of input features or with the model W-LSTM has been spotted for the same network structure and hyper-parameters, and enlarging time step and batch size will improve accuracy and Matthews correlation coefficient despite of number of input and model used in this paper.
Piyush Kumar Yadav, Rajnish Bhasker, Albert Alexander Stonier, Geno Peter · 6 authors
Abstract Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities.
Bitcoin is sometimes called the new gold, replacing gold as a hedge against inflation, and research on the relationship between bitcoin and gold has important practical implications. This paper first calculates the correlation between bitcoin and gold. By introducing the calculation of dynamic penalty coefficient, the double stock portfolio investment problem is transformed into the single stock purchase investment problem, which greatly reduces the difficulty of feature engineering and model application. In terms of decision model, deep reinforcement learning (PPO algorithm) is used to make quantitative investment decisions, and the expected data in SLTM is taken as the input data of deep reinforcement learning, which is combined with deep reinforcement learning. Compared with machine learning quantitative investment decisions, after a period of learning, the accuracy rate and returns have been substantially improved.
Bitcoin is a high-risk asset with a potentially high return. Predicting Bitcoin candlestick, i.e., open, high, low, and close (OHLC) prices, can help investors make trading decisions. This paper aims to explore various data normalization techniques in neural networks to enhance candlestick price prediction. In this study, the sliding window normalization techniques were compared with the whole set normalization techniques for forecasting the daily Bitcoin OHLC prices using two neural network algorithms: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The investigated normalization techniques for both the whole set and the sliding window set included z-score normalization, min-max normalization, and relative change normalization. Historical OHLC prices over several days were used to predict the next day's OHLC prices. The results show that the sliding window normalization techniques outperformed the whole set normalization techniques in terms of RMSE and MAPE with the best technique being the GRU algorithm using the sliding window relative change normalization achieving MAPE of 2.25% and RMSE of 870.52.
Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.
Predicting future demand for distributed energy sources is difficult because of the energy generations is unpredictability. We proposed an auto executable blockchain-based peer-to-peer energy transaction market with Long ShortTerm Memory (LSTM) neural network for energy trading and predictions. Our proposed energy transaction market and trading strategy are built via smart contracts inside the blockchain. The energy trading process in the market is auto executable and does not need a long waiting time as in the conventional bidding strategy. LSTM is integrated into the market for predicting future energy usage. Experimental results show that the proposed blockchain-based peer-to-peer energy transaction market can earn more profit than the traditional method.
Sarat Chandra Nayak, Subhranginee Das, Satchidananda Dehuri, Sung‐Bae Cho
Cryptocurrencies have carved out a significant presence in financial transactions during the past few years. Cryptocurrency market performs similarly to other financial markets with considerable nonlinearity and volatility and its prediction is a growing research area. It is challenging to capture the inherent uncertainties connected with cryptocurrency using the currently used conventional methodologies. The popularity of random vector functional link networks (RVFLN) is attributed to its simple structural layout, quick rate of learning, and enhanced generalization ability. It computes the output layer weights using non-iterative techniques like least square methods or iterative techniques like gradient methods, and assigns hidden neuron parameters at random. Random initialization of non-optimal hidden neuron settings, however, degrades the performance. Population-based metaheuristics are a superior option to random initialization for determining the ideal parameters and avoiding the problem of local optima stagnation. In the current article, an elitist artificial electric field algorithm (eAEFA) for training RVFLN is proposed. Here, eAEFA is utilized to create an ideal RVFLN by determining the weights and biases of the hidden layer connections. The elitism method is used by AEFA to maximize its strength. Here, the most suitable entities are directly inserted to create the population of the following generation. By predicting the closing values of six widely used cryptocurrencies, including Bitcoin, Litecoin, Ethereum, ZEC, XLM, and Ripple, one may determine how well the eAEFA+RVFLN model is performing. For comparison study, models including ARIMA, multi-layer perceptron (MLP), basic RVFLN, support vector regression (SVR), LSTM, GA trained RVFLN, and AEFA trained RVFLN are also constructed concurrently. In terms of performance and statistical significance testing, the suggested eAEFA+RVFLN findings outperform the comparator models. On an average, it achieves a MAPE (mean absolute percentage of error) value of 0.0573, R2(coefficient of determination) of 0.9589, POCID (prediction of change in direction) of 0.9676, RMSE (root mean squared error) of 0.0685, MAE (mean absolute error) of 0.0727 and an average rank of 1.346; as a result, it is possible to recommend it as a useful financial forecasting tool.
The purpose of this research is to compare ARIMA and Prophet algorithms and find the best algorithm for forecasting bitcoin prices. The dataset is two years historical bitcoin data between February 2019 and 2021. The data is segmented into daily, weekly, and monthly period category. Both algorithms are built into a univariate model that only receive 2 features for training the model. Several ARIMA models is developed for each dataset interval. After that, the parameter of each model will be cross-referenced to each other to obtain the best parameter combination. Meanwhile, Prophet model will be developed using automatic and manual tuning. Then again parameter value of each model will be cross-referenced to each other to obtain the best parameter combination. Evaluation of the training model is done by calculating the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Rooted Mean Squared Error (RMSE). The results showed that the best model for the daily and weekly data category was the Prophet algorithm, while for the monthly data category was the ARIMA algorithm.
Aleksandar Petrović, Luka Jovanović, Miodrag Živković, Nebojša Bačanin · 6 authors
The interest for cryptocurrencies is high and hence this work focuses on providing a practical real-world application of the swarm metaheuristics and long short term memory model (LSTM).The goal is price forecasting which is interesting due to the high volatility of the cryptocurrencies.The authors apply LSTM for the solution of the problem which has been proven to reap results with this type of problem.The LSTM is further optimized by a swarm metaheuristic -arithmetic optimization algorithm (AOA).The solution was tested alongside familiar high-performing competitors with the use of standard metrics mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).These metrics have been used for comparison between the solutions, upon which the proposed solution obtained overall best performance that testifies to the improvement of the solution.
In order to accommodate the growing amounts of integrated renewable production, including wind and solar PV, energy networks are undergoing significant adjustments. In recent years, the growth of renewable energy sources (RES) has been greatly accelerated thanks to the privatization and deregulation of the energy industry, as well as financial incentives and energy policy measures. As decentralized energy generation is developed through residential and commercial PV applications, a new position described as an energy prosumer is produced. The traditional division between energy users and producers is eliminated as a result. Blockchain technology automate direct energy transactions inside a distributed system architecture based on consensus-based verification and cryptographic hashing, providing utilities, prosumers, and consumers with a one-of-a-kind, cost-effective, and secure energy-trading system. The objective of this project is to implement a general ABM simulation framework for energy exchange and demonstrate the operation of any blockchain process as well as the expected power profiles of homes. A robust multi-agent structure was constructed and simulated for a Transactive Energy (TE) type Distributed Energy Resources (DER) within the ECCH microgrid that is dependent on blockchain engineering. Auction systems are used in recent blockchain-based LEM plans to balance supply and demand in the future. These blockchain-based LEMs rely on accurate short-term forecasts of the energy generation and consumption of particular homes as a consequence. Such accurate estimations are usually only assumed. In the present study, this assumption was tested by first evaluating the forecast accuracy that can be obtained for particular families using state-of-the-art energy forecasting methods, and then by examining the effects of prediction mistakes on market outcomes in three alternative supply scenarios. The evaluation showed that an LSTM model may provide rather little predicting errors. The prediction process will be modified to fit the design of a blockchain-based LEM. Because of this, the current study differs greatly from past investigations that make a thorough effort to predict the time sequence of smart meters generally.
Summary For the past few years, Bitcoin plays a vital role in both the economical and financial industries. In order to gain a huge return on investment, the investors are eager to forecast the future value of Bitcoin. However, Bitcoin price variation is quite nonlinear and chaotic in nature, so it creates more difficulty in forecasting future value. Researchers found that the multiplicative long short term memory (LSTM) model will be more efficient for predicting those complex variations. So, target mission is about to develop an optimized multiplicative LSTM with an Attention mechanism using Technical Indicators derived from historical data. A modified cuckoo search optimization model is proposed to tune the hyperparameter of the Deep Learning model. This novel optimization algorithm eliminates the local optimum and slower convergence problem of the cuckoo search optimization algorithm. Deibold Mariano test is performed to statistically evaluate the proposed model and it is inferred that the recommended methodology is statistically fit. Regression metrics such as root mean square error, mean square error and mean absolute error has been used for comparative evaluation with related benchmark techniques such as genetic algorithm optimized LSTM (GA–LSTM), particle swarm optimized LSTM (PSO–LSTM) and cuckoo search optimized LSTM (CSO–LSTM). The empirical result shows that the recommended methodology outperforms the taken benchmark models and provides better accuracy.
Joey H. Li, Münür Sacit Herdem, Jatin Nathwani, John Z. Wen
Information technologies involving artificial Intelligence, big data, Internet of Things devices and blockchain have been developed and implemented in many engineering fields worldwide. Existing review articles focus on developments and characteristics of individual topics and the associated deployment in the energy sector. These technologies, all based on communication, information, and data analysis, are naturally coherent and integrable. This article reviews the literature and patents in four closely related fields and aims to provide a holistic view of how they are related and their integrability in relation to smart energy management strategies. Artificial intelligence models forecast energy use and load profiles as well as schedule resources to ensure reliable performance and effective utilization of energy resources. Training artificial intelligence models requires immense volumes of data. Utilizing big data systems and data mining enables the discovery of new functions and relationships, which determines the performance of artificial intelligence. Data mining also refines the information; thus, artificial intelligence is trained iteratively with more accurate data. Smart energy management can be further enhanced through advanced digital technologies like Internet of Things and blockchain. An Internet of Things platform containing edge, fog and cloud layers helps connect artificial intelligence to other hardware and software devices and systems. Furthermore, an Internet of Things platform efficiently transmits and stores data, improving access and availability to stakeholders for data mining. Emerging technologies such as blockchain and cryptocurrency facilitate energy trading and can be designed in the cloud layer of an Internet of Things platform to supplement data storage. Providing an efficient and seamless integration of artificial intelligence, big data, and advanced digital technologies will be an important factor in the emerging transition of the energy sector to a lower-carbon system.