Records with Subject: Intelligent Systems
Showing records 1 to 25 of 60. [First] Page: 1 2 3 Last
Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting
Bartosz Uniejewski, Jakub Nowotarski, Rafał Weron
January 7, 2019 (v1)
Keywords: autoregression, day-ahead market, elastic net, electricity price forecasting, lasso, ridge regression, stepwise regression, variable selection
In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall.
A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection
Ping Jiang, Feng Liu, Yiliao Song
January 7, 2019 (v1)
Keywords: forecasting, fuzzy logic, particle swarm optimization (PSO), reducing volatility, selection rule (SR), self-organizing-map
The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist in the literature, there have been few investigations on improving the forecasting effectiveness of electricity price from the perspective of reducing the volatility of data with satisfactory accuracy. Based on reducing the volatility of the electricity price and the forecasting nature of the radial basis function network (RBFN), this paper successfully develops a two-stage model to forecast the day-ahead electricity price, of which the first stage is particle swarm optimization (PSO)-core mapping (CM) with self-organizing-map and fuzzy set (PCMwSF), and the second stage is selection rule (SR).... [more]
Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China
Jinying Li, Jianfeng Shi, Jinchao Li
January 7, 2019 (v1)
Keywords: Beijing, carbon intensity, IPSO model, scenario analysis
Carbon emissions are the major cause of the global warming; therefore, the exploration of carbon emissions reduction potential is of great significance to reduce carbon emissions. This paper explores the potential of carbon intensity reduction in Beijing in 2020. Based on factors including economic growth, resident population growth, energy structure adjustment, industrial structure adjustment and technical progress, the paper sets 48 development scenarios during the years 2015⁻2020. Then, the back propagation (BP) neural network optimized by improved particle swarm optimization algorithm (IPSO) is used to calculate the carbon emissions and carbon intensity reduction potential under various scenarios for 2016 and 2020. Finally, the contribution of different factors to carbon intensity reduction is compared. The results indicate that Beijing could more than fulfill the 40%⁻45% reduction target for carbon intensity in 2020 in all of the scenarios. Furthermore, energy structure adjustment... [more]
A Review of Classification Problems and Algorithms in Renewable Energy Applications
María Pérez-Ortiz, Silvia Jiménez-Fernández, Pedro A. Gutiérrez, Enrique Alexandre, César Hervás-Martínez, Sancho Salcedo-Sanz
January 7, 2019 (v1)
Keywords: applications, classification algorithms, Machine Learning, Renewable and Sustainable Energy
Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.
Electricity Price Forecasting by Averaging Dynamic Factor Models
Andrés M. Alonso, Guadalupe Bastos, Carolina García-Martos
January 7, 2019 (v1)
Keywords: Bayesian model averaging, dimensionality reduction, electricity prices, forecast combination
In the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in the task, both in the short and long run. However, specifying a single model for the unobserved factors is difficult, and it cannot be guaranteed that such a model exists. In this paper, model averaging is employed to overcome this difficulty, with the expectation that electricity prices would be better forecast by a combination of models for the factors than by a single model. Although our procedure is applicable in other markets, it is illustrated with an application to forecasting spot prices of the Iberian Market, MIBEL (The Iberian Electricity Market). Three combinations of forecasts are successful in providing improved results for alternative forecasting horizons.
A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction
Qunli Wu, Chenyang Peng
January 7, 2019 (v1)
Keywords: cloud-based evolutionary algorithm, least squares support vector machine, paired-sample t-test, two-way comparison, wind power generation prediction
Accurate wind power generation prediction, which has positive implications for making full use of wind energy, seems still a critical issue and a huge challenge. In this paper, a novel hybrid approach has been proposed for wind power generation forecasting in the light of Cloud-Based Evolutionary Algorithm (CBEA) and Least Squares Support Vector Machine (LSSVM). In order to improve the forecasting precision, a two-way comparison approach is conducted to preprocess the original wind power generation data. The pertinent parameters of LSSVM are optimized by using CBEA to verify the learning and generalization abilities of the LSSVM model. The experimental results indicate that the forecasting performance of the proposed model is better than the single LSSVM model and all of the other models for comparison. Moreover, the paired-sample t-test is employed to cast light on the applicability of the developed model.
Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions
Abdullahi Abubakar Mas’ud, Ricardo Albarracín, Jorge Alfredo Ardila-Rey, Firdaus Muhammad-Sukki, Hazlee Azil Illias, Nurul Aini Bani, Abu Bakar Munir
January 7, 2019 (v1)
Keywords: Artificial Intelligence, artificial neural network (ANN), partial discharge (PD)
In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) usin... [more]
A WebGIS Decision Support System for Management of Abandoned Mines
Ranka Stanković, Nikola Vulović, Nikola Lilić, Ivan Obradović, Radule Tošović, Milica Pešić-Georgiadis
January 7, 2019 (v1)
Keywords: abandoned mines, geodatabase, mine reclamation, WebGIS
This paper presents the development of a WebGIS application aimed at providing safe and reliable data needed for reclamation of abandoned mines in national parks and other protected areas in Vojvodina in compliance with existing legal regulations. The geodatabase model for this application has been developed using UML and the CASE tool Microsoft Visio featuring an interface with ArcGIS. The WebGIS application was developed using GeoServer, an open source tool in the Java programming language, with integrated PostgreSQL DB and the possibility of generating and publishing WMS, WFS and KML services. The WebGIS application is publicly available, based on an appropriate central database, which for the first time encompasses all available data on abandoned mines in Vojvodina, and as such may serve as a model for similar databases on the territory of the Republic of Serbia.
Battery Grouping with Time Series Clustering Based on Affinity Propagation
Zhiwei He, Mingyu Gao, Guojin Ma, Yuanyuan Liu, Lijun Tang
January 7, 2019 (v1)
Keywords: affinity propagation, battery grouping, time series clustering, wavelet denoising
Battery grouping is a technology widely used to improve the performance of battery packs. In this paper, we propose a time series clustering based battery grouping method. The proposed method utilizes the whole battery charge/discharge sequence for battery grouping. The time sequences are first denoised with a wavelet denoising technique. The similarity matrix is then computed with the dynamic time warping distance, and finally the time series are clustered with the affinity propagation algorithm according to the calculated similarity matrices. The silhouette index is utilized for assessing the performance of the proposed battery grouping method. Test results show that the proposed battery grouping method is effective.
Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate
Chih-Hong Lin
November 28, 2018 (v1)
Keywords: discrete-type Lyapunov function, permanent magnet synchronous generator, recurrent Chebyshev neural network, wind turbine
A permanent magnet (PM) synchronous generator system driven by wind turbine (WT), connected with smart grid via AC-DC converter and DC-AC converter, are controlled by the novel recurrent Chebyshev neural network (NN) and amended particle swarm optimization (PSO) to regulate output power and output voltage in two power converters in this study. Because a PM synchronous generator system driven by WT is an unknown non-linear and time-varying dynamic system, the on-line training novel recurrent Chebyshev NN control system is developed to regulate DC voltage of the AC-DC converter and AC voltage of the DC-AC converter connected with smart grid. Furthermore, the variable learning rate of the novel recurrent Chebyshev NN is regulated according to discrete-type Lyapunov function for improving the control performance and enhancing convergent speed. Finally, some experimental results are shown to verify the effectiveness of the proposed control method for a WT driving a PM synchronous generator... [more]
Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting
Min-Liang Huang
November 28, 2018 (v1)
Keywords: chaotic quantum particle swarm optimization (CQPSO), electric load forecasting, quantum behavior, support vector regression (SVR)
In existing forecasting research papers support vector regression with chaotic mapping function and evolutionary algorithms have shown their advantages in terms of forecasting accuracy improvement. However, for classical particle swarm optimization (PSO) algorithms, trapping in local optima results in an earlier standstill of the particles and lost activities, thus, its core drawback is that eventually it produces low forecasting accuracy. To continue exploring possible improvements of the PSO algorithm, such as expanding the search space, this paper applies quantum mechanics to empower each particle to possess quantum behavior, to enlarge its search space, then, to improve the forecasting accuracy. This investigation presents a support vector regression (SVR)-based load forecasting model which hybridizes the chaotic mapping function and quantum particle swarm optimization algorithm with a support vector regression model, namely the SVRCQPSO (support vector regression with chaotic quan... [more]
A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines
Paulino José García Nieto, Esperanza García-Gonzalo, Antonio Bernardo Sánchez, Marta Menéndez Fernández
November 28, 2018 (v1)
Keywords: aircraft engine, artificial bee colony (ABC), multivariate adaptive regression splines (MARS), prognostics, reliability, remaining useful life (RUL)
Remaining useful life (RUL) estimation is considered as one of the most central points in the prognostics and health management (PHM). The present paper describes a nonlinear hybrid ABC⁻MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS), which have been successfully adopted for regression problems, with the artificial bee colony (ABC) technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC⁻... [more]
Statistical Analysis of Wave Climate Data Using Mixed Distributions and Extreme Wave Prediction
Wei Li, Jan Isberg, Rafael Waters, Jens Engström, Olle Svensson, Mats Leijon
November 28, 2018 (v1)
Keywords: extreme wave, mixed-distribution model, ocean wave modelling, wave climate, wave energy converter
The investigation of various aspects of the wave climate at a wave energy test site is essential for the development of reliable and efficient wave energy conversion technology. This paper presents studies of the wave climate based on nine years of wave observations from the 2005⁻2013 period measured with a wave measurement buoy at the Lysekil wave energy test site located off the west coast of Sweden. A detailed analysis of the wave statistics is investigated to reveal the characteristics of the wave climate at this specific test site. The long-term extreme waves are estimated from applying the Peak over Threshold (POT) method on the measured wave data. The significant wave height and the maximum wave height at the test site for different return periods are also compared. In this study, a new approach using a mixed-distribution model is proposed to describe the long-term behavior of the significant wave height and it shows an impressive goodness of fit to wave data from the test site.... [more]
High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series
Christopher Jung
November 27, 2018 (v1)
Keywords: annual wind energy yield (AEY), least squares boosting (LSBoost), predictor importance (PI), Wakeby distribution (WK5), wind speed extrapolation
In this paper a methodology is presented that can be used to model the annual wind energy yield (AEYmod) on a high spatial resolution (50 m × 50 m) grid based on long-term (1979⁻2010) near-surface wind speed (US) time series measured at 58 stations of the German Weather Service (DWD). The study area for which AEYmod is quantified is the German federal state of Baden-Wuerttemberg. Comparability of the wind speed time series was ensured by gap filling, homogenization and detrending. The US values were extrapolated to the height 100 m (U100m,emp) above ground level (AGL) by the Hellman power law. All U100m,emp time series were then converted to empirical cumulative distribution functions (CDFemp). 67 theoretical cumulative distribution functions (CDF) were fitted to all CDFemp and their goodness of fit (GoF) was evaluated. It turned out that the five-parameter Wakeby distribution (WK5) is universally applicable in the study area. Prior to the least squares boosting (LSBoost)-based modelin... [more]
A Reconfigurable Formation and Disjoint Hierarchical Routing for Rechargeable Bluetooth Networks
Chih-Min Yu, Yi-Hsiu Lee
November 27, 2018 (v1)
Keywords: bluetooth, energy harvesting, routing, scatternet formation, topology configuration
In this paper, a reconfigurable mesh-tree with a disjoint hierarchical routing protocol for the Bluetooth sensor network is proposed. First, a designated root constructs a tree-shaped subnet and propagates parameters k and c in its downstream direction to determine new roots. Each new root asks its upstream master to start a return connection to convert the first tree-shaped subnet into a mesh-shaped subnet. At the same time, each new root repeats the same procedure as the designated root to build its own tree-shaped subnet, until the whole scatternet is formed. As a result, the reconfigurable mesh-tree constructs a mesh-shaped topology in one densely covered area that is extended by tree-shaped topology to other sparsely covered areas. To locate the optimum k layer for various sizes of networks, a peak-search method is introduced in the designated root to determine the optimum mesh-tree configuration. In addition, the reconfigurable mesh-tree can dynamically compute the optimum layer... [more]
Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm
Qunli Wu, Chenyang Peng
November 27, 2018 (v1)
Keywords: bat algorithm (BA), ensemble empirical mode decomposition (EEMD), grey relational analysis, least squares support vector machine (LSSVM), principal component analysis (PCA)
Regarding the non-stationary and stochastic nature of wind power, wind power generation forecasting plays an essential role in improving the stability and security of the power system when large-scale wind farms are integrated into the whole power grid. Accurate wind power forecasting can make an enormous contribution to the alleviation of the negative impacts on the power system. This study proposes a hybrid wind power generation forecasting model to enhance prediction performance. Ensemble empirical mode decomposition (EEMD) was applied to decompose the original wind power generation series into different sub-series with various frequencies. Principal component analysis (PCA) was employed to reduce the number of inputs without lowering the forecasting accuracy through identifying the variables deemed as significant that maintain most of the comprehensive variability present in the data set. A least squares support vector machine (LSSVM) model with the pertinent parameters being optim... [more]
On Variable Reverse Power Flow-Part II: An Electricity Market Model Considering Wind Station Size and Location
Aouss Gabash, Pu Li
November 27, 2018 (v1)
Keywords: active-reactive energy losses, variable reverse power flow (VRPF), varying power factors (PFs), wind station size-location
This is the second part of a companion paper on variable reverse power flow (VRPF) in active distribution networks (ADNs) with wind stations (WSs). Here, we propose an electricity market model considering agreements between the operator of a medium-voltage active distribution network (MV-ADN) and the operator of a high-voltage transmission network (HV-TN) under different scenarios. The proposed model takes, simultaneously, active and reactive energy prices into consideration. The results from applying this model on a real MV-ADN reveal many interesting facts. For instance, we demonstrate that the reactive power capability of WSs will be never utilized during days with zero wind power and varying limits on power factors (PFs). In contrast, more than 10% of the costs of active energy losses, 15% of the costs of reactive energy losses, and 100% of the costs of reactive energy imported from the HV-TN, respectively, can be reduced if WSs are operated as capacitor banks with no limits on PFs... [more]
Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting
Li-Ling Peng, Guo-Feng Fan, Min-Liang Huang, Wei-Chiang Hong
November 27, 2018 (v1)
Keywords: auto regression, differential empirical mode decomposition, electric load forecasting, Particle Swarm Optimization, quantum theory, support vector regression
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD) method and quantum particle swarm optimization algorithm (QPSO) for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF) and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia) market and the New York Independent System Operator (NYISO, New York, USA) are used for comparing the forecasting perfor... [more]
Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case
Antonio Bello, Javier Reneses, Antonio Muñoz
November 27, 2018 (v1)
Keywords: electricity markets, extremely low prices, hybrid approach, medium-term electricity price forecasting, probabilistic forecasting, spikes
One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according t... [more]
Solar Radiation Forecasting, Accounting for Daily Variability
Roberto Langella, Daniela Proto, Alfredo Testa
November 27, 2018 (v1)
Keywords: daily variability, forecasting, instantaneous variability, parametric distributions, solar irradiance, solar radiation, statistical methods, time series generation
Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters’ data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was fu... [more]
Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting
Tansu Filik
November 27, 2018 (v1)
Keywords: autoregressive moving average model, forecasting, multi-channel, prediction, spatio-temporal, very short-term, wind energy, wind speed
In this paper, the spatio-temporal (multi-channel) linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA) models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and revised. It is shown in detail how to apply these MARMA models to the spatially distributed wind speed measurements. The proposed MARMA models are tested using real wind speed measurements which are collected from the five stations around Canakkale region of Turkey. According to the test results, considerable improvements are observed over the well known persistence, autoregressive (AR) and multi-channel/vector autoregressive (VAR) models. It is also shown that the model can predict wind speed very fast (in milliseconds) which is suitable for the immediate short-term forecasting.
Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model
Yu-Ri Kim, Hae Jin Kang
November 27, 2018 (v1)
Keywords: analysis of variance (ANOVA), energy performance certification, energy simulation, mobile application, prediction model
Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels (i.e., asset rating methodology), a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current st... [more]
Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures
Emilio Gómez-Lázaro, María C. Bueso, Mathieu Kessler, Sergio Martín-Martínez, Jie Zhang, Bri-Mathias Hodge, Angel Molina-García
November 16, 2018 (v1)
Keywords: Akaike information criterion (AIC), Bayesian information criterion (BIC), Weibull distributions, Weibull mixtures, wind power generation
The Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the... [more]
Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm
Yan Hong Chen, Wei-Chiang Hong, Wen Shen, Ning Ning Huang
November 16, 2018 (v1)
Keywords: electric load forecasting, fuzzy c-means (FCM), fuzzy time series (FTS), global harmony search algorithm (GHSA), least squares support vector machine (LSSVM)
This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS) and global harmony search algorithm (GHSA) with least squares support vector machines (LSSVM), namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS) algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA) model and other algorithms hybridized with LSSVM including genetic algorithm (GA), particle swarm optimization (PSO), harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predi... [more]
A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building
Hamid R. Khosravani, María Del Mar Castilla, Manuel Berenguel, Antonio E. Ruano, Pedro M. Ferreira
October 23, 2018 (v1)
Keywords: data selection, electric power demand, multi objective genetic algorithm (MOGA), neural networks, predictive model
Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was desig... [more]
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