Short-Term Power Demand Forecasting Using Blockchain-Based Neural Networks Models

With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hotspot 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

A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. Deep learning models play a vital role in accurately forecasting the energy de-mand. A novel model called Sail Fish Whale Optimization-based Deep Long Short- Term memory (SFWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SFWO is designed by integrating the Sail Fish Optimizer (SFO) with the Whale Optimiza-tion Algorithm (WOA). The Hilbert-Schmidt Independence Criterion Lasso (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm was implemented in MATLAB software package and the study was done using real-time data. The feature selection pro-cess improves the accuracy of the proposed model by training the features using Deep LSTM. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves good accuracy with the average normalized Root Mean Squared Error (RMSE) value of 4.4559. The hybrid approach provides improved accuracy for the prediction of energy demand in India by the year 2047.publishedVersio

Forecasting power of neural networks in cryptocurrency domain : Forecasting the prices of Bitcoin, Ethereum and Cardano with Gated Recurrent Unit and Long Short-Term Memory

Machine learning has developed substantially during the past decades and more emphasis has gone to deeper machine learning methods, i.e., artificial neural networks, computer-based networks seeking to mimic how the human brain functions. The groundwork for ANN research was established already in the 1940s and the advancement of ANNs has been ex-tensive. Price prediction of different financial assets is a broadly studied field, as researchers have been trying to create models to predict the volatile and noisy environment of financial markets. Also, ANNs have been placed for these hard prediction tasks, as their advantage is the ability to find non-linear patterns in uncertain and volatile setting. Cryptocurrencies have made their way to the common audience in the past years. After Nakamoto (2008) presented the first proposal for an electronic cash system, Bitcoin, the number of different cryptocurrencies has exceeded over 8 000. Also, the market capitaliza-tion of all cryptocurrencies has grown rapidly, in November 2021 the aggregate market capi-talization topped 3 000 billion U.S. dollars. Cryptocurrencies are not a small concept for closed groups of tech-people, but a phenomenon that concerns also in the governmental level. This study utilizes recurrent neural networks, GRU and LSTM, in the prediction task regarding cryptocurrencies. In addition to trading data, this study uses Google trend-based popularity score to try to better the ANNs accuracy. In addition to the sole prediction task, the study compares the two used RNN architectures and presents the performance and accuracy with selected performance measures. The results show that recurrent neural networks have potential in prediction tasks in the cryptocurrency domain. The constructed models were able to find coherent trends in the price fluctuations but the average differences on actual and predicted prices were compara-tively high, with the introduced simple RNN models. On average, the LSTM model was able to predict the cryptocurrency prices more accurately, but the GRU model showed also great evidence of prediction accuracy in the domain. All in all, the cryptocurrency prediction task is a hard task due to its volatile nature, but his study shows great evidence for ANNs ability to predict cryptocurrency prices. Considering the findings, further research could be applied to more optimized and complex ANN models as the models used in the study were relatively simple one-layer models.Koneoppiminen on kehittynyt erittäin paljon viimeisten vuosikymmenten aikana, painottuen enemmän syvempien koneoppimisen metodien, kuten keinotekoisten neuroverkkojen (ANN), kehitykseen. Keinotekoiset neuroverkot ovat tietokoneeseen perustuvia verkkoja, jotka pyrkivät jäljittelemään ihmisaivojen toimintaa. Keinotekoisten neuroverkkojen tutki-mus on alkanut jo 1940-luvulla, josta lähtien kyseisten verkkojen kehitys on ollut nopeaa. Eri omaisuuslajien hintakehityksen ennustaminen on laajasti tutkittu alue, kun tutkijat ovat yrit-täneet luoda malleja, joilla he ovat pyrkineet ennustamaan epävarmaa rahoitusmarkkinaym-päristöä. Keinotekoiset neuroverot on valjastettu tähän vaikeaan tehtävän, koska niiden selkeänä etuna on kyky löytää epälineaarisia yhteyksiä epävarmassa ja epävakaassa ympäris-tössä. Viime vuosien aikana kryptovaluutat ovat yleistyneet huomattavasti, niin yksityissijoittajien kun institutionaalisten sijoittajien joukossa. Sen jälkeen, kun Nakamoto (2008) esitteli en-simmäisen ehdotuksen käteisen ja valuutan sähköisestä järjestelmästä, kryptovaluuttojen lukumäärä on kasvanut yli 8 000 yksittäiseen valuuttaan. Samaan aikaan kryptovaluuttojen yhteenlaskettu markkina-arvo on kasvanut räjähdysmäisesti, marraskuussa 2021 kokonais-markkina-arvo kasvoi yli 3 000 miljardiin Yhdysvaltojen dollariin. Nykyään kryptovaluutat eivät ole vain konsepti suljetuille teknologiasta kiinnostuneille ryhmille, vaan ilmiö, joka vaikuttaa myös valtiollisella tasolla. Tämä tutkimus hyödyntää toistuvia neuroverkkoja (recurrent neural networks), GRU ja LSTM, kryptovaluuttojen hintakehityksen ennustamisessa. Kaupankäyntitietojen lisäksi, tut-kimuksessa käytetään Googlen hakutiedusteluihn perustuvaa Google Trend suosiomittaria, neuroverkkojen tarkkuuden parantamiseksi. Kryptovaluuttojen hintakehityksen ennustami-sen lisäksi, tutkimuksessa verrataan kahta RNN-rakennetta ja esitellään molempien verkko-jen tarkkuutta sekä verrataan sitä valituilla tarkkuusmittareilla. Tutkimuksen tulokset osoittavat, että yksinkertaisilla RNN-rakenteilla on selkeää potentiaalia kryptovaluuttojen hintakehityksen ennustamisessa. Tutkimuksessa luodut mallit löytävät johdonmukaisia ja selkeitä trendejä, mutta keskimääräiset erotukset todellisilla ja ennuste-tuilla hinnoilla oli suhteellisesti korkeat. Tutkituista malleista LSTM-malli tuottaa keskimäärin tarkempia ennusteita kuin GRU-malli, mutta erot mallien tarkkuuksissa ovat pienet. Kokonai-suudessaan kryptovaluuttojen hintojen ennustaminen on vaikea tehtävä kryptovaluut-tamarkkinan epävakaan luonteen johdosta, tämä tutkimus kuitenkin osoittaa näyttöä keino-tekoisten neuroverkkojen kyvystä ennustaa kryptovaluuttojen hintoja. Ottaen huomioon tutkimuksen löydökset, lisätutkimusta voisi soveltaa tarkemmin optimoituihin ja kompleksi-simpiin keinotekoisiin neuroverkkoihin, sillä tässä tutkimuksessa käytetyt mallit olivat suh-teellisen yksinkertaisia

Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning

One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of "blockchain" as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.Comment: 17 pages, 5 figure

Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms

Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) 1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector

Patterns of mobility in a smart city

Transportation data in smart cities is becoming increasingly available. This data allows building meaningful, intelligent solutions for city residents and city management authorities, the so-called Intelligent Transportation Systems. Our research focused on Lisbon mobility data, provided by Lisbon municipality. The main research objective was to address mobility problems, interdependence, and cascading effects solutions for the city of Lisbon. We developed a data-driven approach based on historical data with a strong focus on visualization methods and dashboard creation. Also, we applied a method based on time series to do prediction based on the traffic congestion data provided. A CRISP-DM approach was applied, integrating different data sources, using Python. Hence, understand traffic patterns, and help the city authorities in the decision-making process, namely more preparedness, adaptability, responsiveness to events.Os dados de transporte, no âmbito das cidades inteligentes, estão cada vez mais disponíveis. Estes dados permitem a construção de soluções inteligentes com impacto significativo na vida dos residentes e nos mecanismos das autoridades de gestão da cidade, os chamados Sistemas de Transporte Inteligentes. A nossa investigação incidiu sobre os dados de mobilidade urbana da cidade de Lisboa, disponibilizados pelo município. O principal objetivo da pesquisa foi abordar os problemas de mobilidade, interdependência e soluções de efeitos em cascata para a cidade de Lisboa. Para alcançar este objetivo foi desenvolvida uma metodologia baseada nos dados históricos do transito no centro urbano da cidade e principais acessos, com uma forte componente de visualização. Foi também aplicado um método baseado em series temporais para fazer a previsão das ocorrências de transito na cidade de Lisboa. Foi aplicada uma abordagem CRISP-DM, integrando diferentes fontes de dados, utilizando Python. Esta tese tem como objetivo identificar padrões de mobilidade urbana com análise e visualização de dados, de forma a auxiliar as autoridades municipais no processo de tomada de decisão, nomeadamente estar mais preparada, adaptada e responsiva

Applied Data Science Approaches in FinTech: Innovative Models for Bitcoin Price Dynamics

Living in a data-intensive environment is a natural consequence to the continuous innovations and technological advancements, that created countless opportunities for addressing domain-specific challenges following the Data Science approach. The main objective of this thesis is to present applied Data Science approaches in FinTech, focusing on proposing innovative descriptive and predictive models for studying and exploring Bitcoin Price Dynamics and Bitcoin Price Prediction. With reference to the research area of Bitcoin Price Dynamics, two models are proposed. The first model is a Network Vector Autoregressive model that explains the dynamics of Bitcoin prices, based on a correlation network Vector Autoregressive process that models interconnections between Bitcoin prices from different exchange markets and classical assets prices. The empirical findings show that Bitcoin prices from different markets are highly interrelated, as in an efficiently integrated market, with prices from larger and/or more connected exchange markets driving other prices. The results confirm that Bitcoin prices are unrelated with classical market prices, thus, supporting the diversification benefit property of Bitcoin. The proposed model can predict Bitcoin prices with an error rate of about 11% of the average price. The second proposed model is a Hidden Markov Model that explains the observed time dynamics of Bitcoin prices from different exchange markets, by means of the latent time dynamics of a predefined number of hidden states, to model regime switches between different price vectors, going from "bear'' to "stable'' and "bear'' times. Structured with three hidden states and a diagonal variance-covariance matrix, the model proves that the first hidden state is concentrated in the initial time period where Bitcoin was relatively new and its prices were barely increasing, the second hidden state is mostly concentrated in a period where Bitcoin prices were steadily increasing, while the third hidden state is mostly concentrated in the last period where Bitcoin prices witnessed a high rate of volatility. Moreover, the model shows a good predictive performance when implemented on an out of sample dataset, compared to the same model structured with a full variance-covariance matrix. The third and final proposed model, falls within the area of Bitcoin Price Prediction. A Hybrid Hidden Markov Model and Genetic Algorithm Optimized Long Short Term Memory Network is proposed, aiming at predicting Bitcoin prices accurately, by introducing new features that are not usually considered in the literature. Moreover, to compare the performance of the proposed model to other models, a more traditional ARIMA model has been implemented, as well as a conventional Genetic Algorithm-optimized Long Short Term Memory Network. With a mean squared error of 33.888, a root mean squared error of 5.821 and a mean absolute error of 2.510, the proposed model achieves the lowest errors among all the implemented models, which proves its effectiveness in predicting Bitcoin prices