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Records with Subject: Intelligent Systems
Showing records 1 to 25 of 38. [First] Page: 1 2 Last
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]
A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks
Guoqiang Sun, Tong Chen, Zhinong Wei, Yonghui Sun, Haixiang Zang, Sheng Chen
October 23, 2018 (v1)
Keywords: carbon price forecasting, comprehensive evaluation criteria, partial autocorrelation function (PACF), spiking neural network (SNN), variational mode decomposition (VMD)
Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on variational mode decomposition (VMD) and spiking neural networks (SNNs) is proposed. An original carbon price series is firstly decomposed into a series of relatively stable components through VMD to simplify the interference and coupling across characteristic information of different scales in the data. Then, a SNN forecasting model is built for each component, and the partial autocorrelation function (PACF) is used to determine the input variables for each SNN model. The final forecasting result for the original carbon price can be obtained by aggregating the forecasting results of all the components. Actual... [more]
A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting
Zhaoxuan Li, SM Mahbobur Rahman, Rolando Vega, Bing Dong
October 23, 2018 (v1)
Keywords: artificial neural network (ANN), photovoltaic (PV) forecasting, support vector regression (SVR)
We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.
A Novel Geographical Information Systems Framework to Characterize Photovoltaic Deployment in the UK: Initial Evidence
Paul Westacott, Chiara Candelise
October 23, 2018 (v1)
Keywords: distributed generation, geographical information systems (GIS), photovoltaics (PV), renewable energy policy
Globally, deployment of grid-connected photovoltaics (PV) has increased dramatically in recent years. The UK has seen rapid uptake reaching over 500,000 installations totalling 2.8 GWp by 2013. PV can be installed in different market segments (domestic rooftop, non-domestic rooftop and ground-mounted “solar-farms”) covering a broad range of system sizes in a high number of locations. It is important to gain detailed understanding of what grid-connected PV deployment looks like (e.g., how it deployed across different geographic areas and market segments), and identify the major drivers behind it. This paper answers these questions by developing a novel geographical information systems (GIS)-framework—the United Kingdom Photovoltaics Database (UKPVD)—to analyze temporal and spatial PV deployment trends at high resolution across all market segments. Results show how PV deployment changed over time with the evolution of governmental PV policy support. Then spatial trends as function of loc... [more]
A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems
Yanzi Wang, Weida Wang, Yulong Zhao, Lei Yang, Wenjun Chen
October 23, 2018 (v1)
Keywords: battery, fuzzy logic, hybrid energy storage system (HESS), Markov random prediction, ultracapacitor (UC)
Over the last few years; issues regarding the use of hybrid energy storage systems (HESSs) in hybrid electric vehicles have been highlighted by the industry and in academic fields. This paper proposes a fuzzy-logic power management strategy based on Markov random prediction for an active parallel battery-UC HESS. The proposed power management strategy; the inputs for which are the vehicle speed; the current electric power demand and the predicted electric power demand; is used to distribute the electrical power between the battery bank and the UC bank. In this way; the battery bank power is limited to a certain range; and the peak and average charge/discharge power of the battery bank and overall loss incurred by the whole HESS are also reduced. Simulations and scaled-down experimental platforms are constructed to verify the proposed power management strategy. The simulations and experimental results demonstrate the advantages; feasibility and effectiveness of the fuzzy-logic power man... [more]
A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination
Lianhui Li, Chunyang Mu, Shaohu Ding, Zheng Wang, Runyang Mo, Yongfeng Song
October 23, 2018 (v1)
Keywords: combination forecast, immune algorithm, load forecasting, Markov chain, normal cloud model, Particle Swarm Optimization, robustness
Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study i... [more]
Methods for Global Survey of Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data
Christopher D. Elvidge, Mikhail Zhizhin, Kimberly Baugh, Feng-Chi Hsu, Tilottama Ghosh
October 23, 2018 (v1)
Keywords: carbon dioxide emissions, carbon intensity, gas flaring, Nightfire, Visible Infrared Imaging Radiometer Suite (VIIRS)
A set of methods are presented for the global survey of natural gas flaring using data collected by the National Aeronautics and Space Administration/National Oceanic and Atmospheric Administration NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The accuracy of the flared gas volume estimates is rated at ±9.5%. VIIRS is particularly well suited for detecting and measuring the radiant emissions from gas flares through the collection of shortwave and near-infrared data at night, recording the peak radiant emissions from flares. In 2012, a total of 7467 individual flare sites were identified. The total flared gas volume is estimated at 143 (±13.6) billion cubic meters (BCM), corresponding to 3.5% of global production. While the USA has the largest number of flares, Russia leads in terms of flared gas volume. Ninety percent of the flared gas volume was found in upstream production areas, 8% at refineries and 2% at liquified natural gas (LNG) terminals. The results confirm that... [more]
A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks
Honglu Zhu, Xu Li, Qiao Sun, Ling Nie, Jianxi Yao, Gang Zhao
October 22, 2018 (v1)
Keywords: artificial neural network, photovoltaic power prediction, signal reconstruction, theoretical solar irradiance, wavelet decomposition
The power prediction for photovoltaic (PV) power plants has significant importance for their grid connection. Due to PV power’s periodicity and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable. This paper presents a method combining the advantages of the wavelet decomposition (WD) and artificial neural network (ANN) to solve this problem. With the ability of ANN to address nonlinear relationships, theoretical solar irradiance and meteorological variables are chosen as the input of the hybrid model based on WD and ANN. The output power of the PV plant is decomposed using WD to separated useful information from disturbances. The ANNs are used to build the models of the decomposed PV output power. Finally, the outputs of the ANN models are reconstructed into the forecasted PV plant power. The presented method is compared with the traditional forecasting method based on ANN. The results shows that the method... [more]
Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation
Erdong Zhao, Jing Zhao, Liwei Liu, Zhongyue Su, Ning An
October 22, 2018 (v1)
Keywords: ARIMAX, self-adaptive strategy, wind speed, WRF simulation
Wind speed forecasting is difficult not only because of the influence of atmospheric dynamics but also for the impossibility of providing an accurate prediction with traditional statistical forecasting models that work by discovering an inner relationship within historical records. This paper develops a self-adaptive (SA) auto-regressive integrated moving average with exogenous variables (ARIMAX) model that is optimized very-short-term by the chaotic particle swarm optimization (CPSO) algorithm, known as the SA-ARIMA-CPSO approach, for wind speed prediction. The ARIMAX model chooses the wind speed result from the Weather Research and Forecasting (WRF) simulation as an exogenous input variable. Further, an SA strategy is applied to the ARIMAX process. When new information is available, the model process can be updated adaptively with parameters optimized by the CPSO algorithm. The proposed SA-ARIMA-CPSO approach enables the forecasting process to update training information and model pa... [more]
Error Assessment of Solar Irradiance Forecasts and AC Power from Energy Conversion Model in Grid-Connected Photovoltaic Systems
Gianfranco Chicco, Valeria Cocina, Paolo Di Leo, Filippo Spertino, Alessandro Massi Pavan
October 22, 2018 (v1)
Keywords: distributed generation, error assessment, irradiance spike, photovoltaic (PV) conversion model, photovoltaic systems, power profiles, Renewable and Sustainable Energy, weather forecasts
Availability of effective estimation of the power profiles of photovoltaic systems is essential for studying how to increase the share of intermittent renewable sources in the electricity mix of many countries. For this purpose, weather forecasts, together with historical data of the meteorological quantities, provide fundamental information. The weak point of the forecasts depends on variable sky conditions, when the clouds successively cover and uncover the solar disc. This causes remarkable positive and negative variations in the irradiance pattern measured at the photovoltaic (PV) site location. This paper starts from 1 to 3 days-ahead solar irradiance forecasts available during one year, with a few points for each day. These forecasts are interpolated to obtain more irradiance estimations per day. The estimated irradiance data are used to classify the sky conditions into clear, variable or cloudy. The results are compared with the outcomes of the same classification carried out wi... [more]
The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company
Umut Ugurlu, Oktay Tas, Aycan Kaya, Ilkay Oksuz
October 4, 2018 (v1)
Keywords: electricity price forecasting, hydro-based generation company, mixed integer linear programming, profit loss, self-scheduling
Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important loss of profits to the suppliers. In this paper, the financial effect of inaccurate electricity price forecasts on a hydro-based GenCo is examined. Electricity price forecasts of five individual and four hybrid forecast models and the ex-post actual prices are used to schedule the hydro-based GenCo using Mixed Integer Linear Programming (MILP). The financial effect measures of profit loss, Economic Loss Index (ELI) and Price Forecast Disadvantage Index (PFDI), as well as Mean Absolute Error (MAE) of the models are used for comparison of the data from 24 weeks of the year. According to the results, a hybrid model, 50% Artificial Neural Network (ANN)⁻50% Long Short Term Memory (LSTM), has the best performance in terms of financial effect. Furth... [more]
Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique
Narongrit Pimkumwong, Ming-Shyan Wang
September 21, 2018 (v1)
Keywords: artificial neural network, direct torque control, induction motor drives, speed estimation
This paper presents the speed estimator for speed sensorless direct torque control of a three-phase induction motor based on constant voltage per frequency (V/F) control technique, using artificial neural network (ANN). The estimated stator current equation is derived and rearranged consistent with the control algorithm and ANN structure. For the speed estimation, a weight in ANN, which relates to the speed, is adjusted by using Widrow⁻Hoff learning rule to minimize the sum of squared errors between the measured stator current and the estimated stator current from ANN output. The consequence of using this method leads to the ability of online speed estimation and simple ANN structure. The simulation and experimental results in high- and low-speed regions have confirmed the validity of the proposed speed estimation method.
Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting
Yusen Wang, Wenlong Liao, Yuqing Chang
September 21, 2018 (v1)
Keywords: GRU network, K-means, Pearson coefficient, photovoltaic power forecasting
Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photo... [more]
Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network
Jiao Liu, Jinfu Liu, Daren Yu, Myeongsu Kang, Weizhong Yan, Zhongqi Wang, Michael G. Pecht
September 21, 2018 (v1)
Keywords: convolutional neural network (CNN), exhaust gas temperature (EGT), Fault Detection, gas turbine, hot component
Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot... [more]
Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction
Li Han, Rongchang Zhang, Xuesong Wang, Yu Dong
September 21, 2018 (v1)
Keywords: battery energy storage system, factor feature extraction, multi-time scale rolling dispatch, real-time error compensation, wind power accommodation, wind power forecast error
This paper looks at the ability to cope with the uncertainty of wind power and reduce the impact of wind power forecast error (WPFE) on the operation and dispatch of power system. Therefore, several factors which are related to WPFE will be studied. By statistical analysis of the historical data, an indicator of real-time error based on these factors is obtained to estimate WPFE. Based on the real-time estimation of WPFE, a multi-time scale rolling dispatch model for wind/storage power system is established. In the real-time error compensation section of this model, the previous dispatch plan of thermal power unit is revised according to the estimation of WPFE. As the regulating capacity of thermal power unit within a short time period is limited, the estimation of WPFE is further compensated by using battery energy storage system. This can not only decrease the risk caused by the wind power uncertainty and lessen wind spillage, but also reduce the total cost. Thereby providing a new m... [more]
Application of Markov Model to Estimate Individual Condition Parameters for Transformers
Amran Mohd Selva, Norhafiz Azis, Muhammad Sharil Yahaya, Mohd Zainal Abidin Ab Kadir, Jasronita Jasni, Young Zaidey Yang Ghazali, Mohd Aizam Talib
September 21, 2018 (v1)
Keywords: Chi-square test, condition parameters estimation, Condition-Based Monitoring (CBM), Markov Model (MM), non-linear optimization, percentage of absolute error
This paper presents a study to estimate individual condition parameters of the transformer population based on Markov Model (MM). The condition parameters under study were hydrogen (H₂), methane (CH₄), acetylene (C₂H₂), ethylene (C₂H₄), ethane (C₂H₆), carbon monoxide (CO), carbon dioxide (CO₂), dielectric breakdown voltage, interfacial tension, colour, acidity, water content, and 2-furfuraldehyde (2-FAL). First, the individual condition parameter of the transformer population was ranked and sorted based on recommended limits as per IEEE Std. C57. 104-2008 and IEEE Std. C57.106-2015. Next, the mean for each of the condition parameters was computed and the transition probabilities for each condition parameters were obtained based on non-linear optimization technique. Next, the future states probability distribution was computed based on the MM prediction model. Chi-square test and percentage of absolute error analysis were carried out to find the goodness-of-fit between predicted and com... [more]
A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents
Bingtuan Gao, Xiaofeng Liu, Zhenyu Zhu
September 21, 2018 (v1)
Keywords: bottom-up, consumption behavior, household load profile, similar day
The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances’ switch sta... [more]
SOLIS—A Novel Decision Support Tool for the Assessment of Solar Radiation in ArcGIS
Jan K. Kazak, Małgorzata Świąder
September 21, 2018 (v1)
Keywords: ArcGIS, decision support system, photovoltaics, Renewable Energy, Simulation, solar radiation, sustainable development
The global Sustainable Development Goals influence the implementation of energy development strategies worldwide. However, in order to support local stakeholders in sustainable energy development strategies and climate change adaptation plans and the implementation of policies, there is a need to equip local decision makers with tools enabling the assessment of sustainable energy investments. In order to do so, the aim of this study is to create a novel tool for the assessment of solar radiation (SOLIS) in ArcGIS. The SOLIS tool builds on the existing ArcGIS algorithm by including input data conversion and post-processing of the results. This should expand the group of potential users of solar radiation analyses. The self-filtering tool excludes surfaces that are not suitable for solar energy investments due to geometrical reasons. The reduction of the size of the output data is positive for technical reasons (speed of the calculation and occupied storage place) and for cognitive reaso... [more]
Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs
Chenhua Ni, Xiandong Ma
September 21, 2018 (v1)
Keywords: artificial neural network, convolutional neural network, deep learning, ocean energy, power prediction, wave energy converter
Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposed approach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for co... [more]
Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting
Miguel López, Carlos Sans, Sergio Valero, Carolina Senabre
September 21, 2018 (v1)
Keywords: artificial intelligence (AI), neural networks, short-term load forecasting (STLF)
Artificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF) in the last 20 years and it has partly displaced older time-series and statistical methods to a second row. However, the STLF problem is very particular and specific to each case and, while there are many papers about AI applications, there is little research determining which features of an STLF system is better suited for a specific data set. In many occasions both classical and modern methods coexist, providing combined forecasts that outperform the individual ones. This paper presents a thorough empirical comparison between Neural Networks (NN) and Autoregressive (AR) models as forecasting engines. The objective of this paper is to determine the circumstances under which each model shows a better performance. It analyzes one of the models currently in use at the National Transport System Operator in Spain, Red Eléctrica de España (REE), which combines both techniques. The parameters that are tes... [more]
The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy
Pruethsan Sutthichaimethee, Kuskana Kubaha
September 21, 2018 (v1)
Keywords: economic growth and the environment, error correction mechanism model, final energy consumption, long-term, LT-ARIMAXS model, sustainable development
Presently, Thailand runs various sustainable development-based policies to boost the growth in economy, society, and environment. In this study, the economic and social growth was found to continuously increase and negatively deteriorate the environment at the same time due to a more massive final energy consumption in the petroleum industries sector than any other sectors. Therefore, it is necessary to establish national planning and it requires an effective forecasting model to support Thailand’s policy-making. This study aimed to construct a forecasting model for a final energy consumption prediction in Thailand’s petroleum industry sector for a longer-term (2018⁻2037) at a maximum efficiency from a certain class of methods. The Long Term-Autoregressive Integrated Moving Average with Exogeneous variables and Error Correction Mechanism model (LT-ARIMAXS model) (p, d, q, Xi, ECT(t−1)) was adapted from the autoregressive and moving average model incorporating influential variables toge... [more]
Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models
Bartosz Uniejewski, Rafał Weron
September 21, 2018 (v1)
Keywords: automated variable selection, day-ahead market, electricity spot price, LASSO, long-term seasonal component, variance stabilizing transformation
Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify... [more]
Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees
María del Carmen Ruiz-Abellón, Antonio Gabaldón, Antonio Guillamón
September 21, 2018 (v1)
Keywords: direct market consumers, Electricity Markets, ensemble methods, load forecasting models, regression trees
Load forecasting models are of great importance in Electricity Markets and a wide range of techniques have been developed according to the objective being pursued. The increase of smart meters in different sectors (residential, commercial, universities, etc.) allows accessing the electricity consumption nearly in real time and provides those customers with large datasets that contain valuable information. In this context, supervised machine learning methods play an essential role. The purpose of the present study is to evaluate the effectiveness of using ensemble methods based on regression trees in short-term load forecasting. To illustrate this task, four methods (bagging, random forest, conditional forest, and boosting) are applied to historical load data of a campus university in Cartagena (Spain). In addition to temperature, calendar variables as well as different types of special days are considered as predictors to improve the predictions. Finally, a real application to the Span... [more]
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