scholarly journals A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

2021 ◽  
Vol 13 (22) ◽  
pp. 12693
Author(s):  
Adnan Yousaf ◽  
Rao Muhammad Asif ◽  
Mustafa Shakir ◽  
Ateeq Ur Rehman ◽  
Fawaz Alassery ◽  
...  
Keyword(s):  
Time Series ◽  
Energy Management ◽  
Principal Component ◽  
Computational Time ◽  
Percentage Error ◽  
Price Forecasting ◽  
Primary Concern ◽  
Purchase Price ◽  
Energy Management Systems ◽  
Auto Regression

Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from National Electric Power Regulatory Authority (NEPRA) are considered for MAPE reduction in PF. Eight time-series and auto-regression algorithms are developed for data fetching and setting the objective function. The feed-forward ANFIS based on the ML approach and space vector regression (SVR) is introduced to PF by taking the input from time series and auto-regression (AR) algorithms. Best-feature selection is conducted by adopting the Binary Genetic Algorithm (BGA)-Principal Component Analysis (PCA) approach that ultimately minimizes the complexity and computational time of the model. The proposed integration strategy computes the mean absolute percentage error (MAPE), and the overall improvement percentage is 9.24%, which is valuable in price forecasting of the energy management system (EMS). In the end, EMS based on the Firefly algorithm (FA) has been presented, and by implementing FA, the cost of electricity has been reduced by 21%, 19%, and 20% for building 1, 2, and 3, respectively.

scholarly journals Gold Price Forecasting using Box-Jenkins Method

2019 ◽  
Vol 1 (3) ◽  
pp. 15-27
Author(s):  
Nur Atikah Khalid ◽  
Nurfadhlina Abdul Halim
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Time Series Analysis ◽  
Commodity Prices ◽  
Commodity Price ◽  
Percentage Error ◽  
Price Forecasting ◽  
Analysis Method ◽  
Current Trends ◽  
Future Value

In general, the nature of gold that acts as a hedge against inflation and its stable price over the course of the financial crisis has made it a unique commodity. Price forecasts are a must for gold producers, investors and central bank to know the current trends in gold prices. Forecasting the future value of a variable is often done with time series analysis method. This study was conducted to determine the best model for forecasting gold commodity prices as well as forecasting world gold commodity prices in 2018 using Box-Jenkins approach. The data used in this study was obtained from Investing.com from 2015 until 2017. This study shows that ARIMA (1,1,1) is the best model to predict gold commodity prices based on Mean Absolute Percentage Error (MAPE). MAPE value for ARIMA (1,1,1) is 0.02%, where this value proves that forecasting using ARIMA (1,1,1) is the best forecasting because MAPE value is less than 10%.

scholarly journals Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems

Inventions ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Karol Bot ◽  
Antonio Ruano ◽  
Maria da Graça Ruano
Keyword(s):  
Energy Management ◽  
Management Systems ◽  
Percentage Error ◽  
Average Error ◽  
Short Term ◽  
Energy Management Systems ◽  
Multi Objective Genetic Algorithm ◽  
Home Energy Management ◽  
The Usa ◽  
Short Term Forecasting

Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.

scholarly journals Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Crude Oil ◽  
Oil Prices ◽  
Principal Component ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Swarm Optimization ◽  
Crude Oil Prices

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

scholarly journals STOCK MARKET STATISTICAL DATA ANALYSIS FOR PRICES FORECASTING AND TRADING DECISION SUPPORT

InterConf ◽  
2021 ◽  
pp. 283-288
Author(s):  
Галина Грінберг
Keyword(s):  
Time Series ◽  
Computer Simulation ◽  
Stock Market ◽  
Stock Price ◽  
Statistical Data ◽  
Price Forecasting ◽  
Wave Component ◽  
Statistical Data Analysis ◽  
Decomposition Approach ◽  
Auto Regression

A general problems and methods for stock market statistical analysis are analyzed. A new method for stock price forecasting problem is considered based on a time series structural decomposition approach realized in special assignment of wave component auto-regression model as a superposition of harmonics with tuning frequencies. Computer simulation has been fulfilled in order to evaluate the performance of proposed method and algorithms.

scholarly journals The User’s Perspective on Home Energy Management Systems

2018 ◽  
Author(s):  
Ad Straub ◽  
Ellard Volmer
Keyword(s):  
Energy Saving ◽  
Energy Management ◽  
Principal Component ◽  
Added Value ◽  
Management Systems ◽  
Q Method ◽  
Single Family ◽  
Energy Management Systems ◽  
Home Energy Management ◽  
Saving Effect

In contrast to physical sustainable measures carried out in homes, such as insulation, the installation of a Home Energy Management System (HEMS) has no direct and immediate energy-saving effect. A HEMS gives insight into resident behaviour regarding energy use. When this is linked to the appropriate feedback, the resident is in a position to change his or her behaviour. This should result in reduced gas and/or electricity consumption. The aim of our study is to contribute towards the effective use of home energy management systems (HEMS) by identifying types of homeowners in relation to the use of HEMS. The research methods used were a literature review and the Q-method. A survey using the Q-method was conducted among 39 owners of single-family homes in various Rotterdam neighbourhoods. In order to find shared views among respondents, a principal component analysis (PCA) was performed. Five different types of homeowner could be distinguished: the optimists, the privacy-conscious, the technicians, the sceptics, and the indifferent. Their opinions vary as regards the added value of a HEMS, what characteristics a HEMS should have, how much confidence they have in the energy-saving effect of such systems, and their views on the privacy and safety of HEMS. The target group classification can be used as input for a way in which local stakeholders, e.g. a municipality, can offer HEMS that is in line with the wishes of the homeowner.

scholarly journals Utilization of Time Series Tools in Life-sciences and Neuroscience

2020 ◽  
Vol 15 ◽  
pp. 263310552096304
Author(s):  
Harshit Gujral ◽  
Ajay Kumar Kushwaha ◽  
Sukant Khurana
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Regression Models ◽  
Markov Models ◽  
Principal Component ◽  
Science Research ◽  
Information Technology Professionals ◽  
Stack Overflow ◽  
Neuroscience Research ◽  
Auto Regression

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals’, other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Expectation-Maximization Principal Component Analysis

2020 ◽  
Author(s):  
wudong li ◽  
weiping jiang
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Missing Data ◽  
Expectation Maximization ◽  
Principal Component ◽  
Component Analysis ◽  
Computational Time ◽  
Common Mode ◽  
Position Time Series ◽  
Gnss Position Time Series

<p>Removal of the Common Mode Error (CME) is very important for the investigation of Global Navigation Satellite Systems (GNSS) technique error and the estimation of accurate GNSS velocity field for geodynamic applications. The commonly used spatiotemporal filtering methods cannot accommodate missing data, or they have high computational complexity when dealing with incomplete data. This research presents the Expectation-Maximization Principal Component Analysis (EMPCA) to estimate and extract CME from the incomplete GNSS position time series. The EMPCA method utilizes an Expectation-Maximization iterative algorithm to search each principal subspace, which allows extracting a few eigenvectors and eigenvalues without covariance matrix and eigenvalue decomposition computation. Moreover, it could straightforwardly handle the missing data by Maximum Likelihood Estimation (MLE) at each iteration. To evaluate the performance of the EMPCA algorithm for extracting CME, 44 continuous GNSS stations located in Southern California have been selected here. Compared to previous approaches, EMPCA could achieve better performance using less computational time and exhibit slightly lower CME relative errors when more missing data exists. Since the first Principal Component (PC) extracted by EMPCA is remarkably larger than the other components, and its corresponding spatial response presents nearly uniform distribution, we only use the first PC and its eigenvector to reconstruct the CME for each station. After filtering out CME, the interstation correlation coefficients are significantly reduced from 0.46, 0.49, 0.42 to 0.18, 0.17, 0.13 for the North, East, and Up (NEU) components, respectively. The Root Mean Square (RMS) values of the residual time series and the colored noise amplitudes for the NEU components are also greatly suppressed, with an average reduction of 25.9%, 27.4%, 23.3% for the former, and 49.7%, 53.9%, and 48.9% for the latter. Moreover, the velocity estimates are more reliable and precise after removing CME, with an average uncertainty reduction of 52.3%, 57.5%, and 50.8% for the NEU components, respectively. All these results indicate that the EMPCA method is an alternative and more efficient way to extract CME from regional GNSS position time series in the presence of missing data. Further work is still required to consider the effect of formal errors on the CME extraction during the EMPCA implementation.</p>

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