110 research outputs found

    Week Ahead Electricity Price Forecasting Using Artificial Bee Colony Optimized Extreme Learning Machine with Wavelet Decomposition

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    Electricity price forecasting is one of the more complex processes, due to its non-linearity and highly varying nature. However, in today\u27s deregulated market and smart grid environment, the forecasted price is one of the important data sources used by producers in the bidding process. It also helps the consumer know the hourly price in order to manage the monthly electricity price. In this paper, a novel electricity price forecasting method is presented, based on the Artificial Bee Colony optimized Extreme Learning Machine (ABC-ELM) with wavelet decomposition technique. This has been attempted with two different input data formats. Each data format is decomposed using wavelet decomposition, Daubechies Db4 at level 6; all the decomposed data are forecasted using the proposed method and aggregate is formed for the final prediction. This prediction has been attempted in three different electricity markets, in Finland, Switzerland and India. The forecasted values of the three different countries, using the proposed method are compared with various other methods, using graph plots and error metrics and the proposed method is found to provide better accuracy

    Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

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    Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

    State of the art of machine learning models in energy systems: A systematic review

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    Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability

    Development and evaluation of data-driven models for electricity demand forecasting in Queensland, Australia

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    Queensland (QLD) is the second largest state in Australia, with a growing demand for electricity, but existing studies appear to lack their ability to accurately model the consumer demand for electricity. In this Master of Science Research (MSCR) thesis, two kinds of hybrid forecasting models were developed by integrating the Extreme Learning Machines (ELM) with a Markov Chain Monte Carlo (MCMC) algorithm based bivariate copula model (ELM-MCMC) and also, a conditional bivariate copula model to probabilistically forecast the electricity demand (D). The study has incorporated statistically significant lagged electricity price (PR) datasets as a non-linear regression covariate into the final D-forecasting model. In the first objective of the MSCR thesis, the ELM model was trained using statistically significant historical electricity demand at (t–1) timesteps for the state of Queensland used as a predictor variable, derived from Partial Autocorrelation Functions (PACF). This represented historical usage patterns in the electricity demand datasets used to forecast the future usage. It was then tested against current electricity demand (D(t)) to forecast the future D values. The output (i.e., simulated and observed tested D values) from the independent test dataset of the ELM model was used as the input for the MCMC-based copula model to derive the best copula model and to further improve forecasting accuracy. This involved the adoption of twenty-six copulas (e.g., Gaussian, t, Clayton, Gumble, Frank, etc.) and enabled us to also rank the best copulas based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Maximum Likelihood (MaxL) to establish the dependence of historical D with the current and future D values. The results for the ELM-MCMC copulabased model outperformed both of its counterpart models (i.e. MCMC copula-based model and the standalone ELM model) based on vigorous statistical performance metrics. For 6 and 12-hours timescales, the MCMC-Fischer-Hinzmann copula yielded the highest Legates and McCabe Index (LM) (0.98 and 0.98), and lowest error terms including root mean square error (RMSE) (285.480 and 534.090), relative root mean square error (RRMSE) (0.348 and 0.320%), mean absolute error (MAE) (262.241 and 490.661 MW), relative mean absolute error (RMAE) (0.336 and 0.309 %), AIC (-63136.102 and -34727.466), BIC (-63125.530 and -34718.279), and MaxL ( 51570.051 and 17365.733), respectively. Similarly, for the daily timescale, the ELM-MCMC-Cuadras-Auge copula outclassed its counterpart models by displaying LM (0.98), MSE ( 482703.8 MW), RMSE (694.769 MW), RRMSE (0.220 %), MAE (638.365 MW), RMAE (0.208 %), AIC (-14514.312), BIC (-14510.412), and MaxL (7258.156). These present results indicated that the hybrid ELM-MCMC copula-based model had an excellent performance, evidenced by attaining less than 10% RRMSE and RMAE, and Legates McCabe value close to unity. This is further supported by better model fits as denoted by lower AIC and BIC values and small residual error between observed and predicted data as indicated in higher MaxL values for the respective timescales. In another phase of this study, we explored the ability of both local and global optimization techniques in achieving the best parameter estimate for the 26 copulas. It has shown that the global MCMC optimization method delivers accurate parameter estimates for 6 and 12-hours timescales whilst presenting information on the posterior distribution by computing uncertainty range of parameter values within a Bayesian framework. The local method appeared to provide better estimates of copula parameters for the daily timescale of D-forecasting. In the second objective of the MSCR thesis, this study has developed a conditional bivariate copula model to probabilistically forecast electricity demand by incorporating the significant lagged electricity price (PR) from the Australian Energy Market Operator (AEMO) as a covariate into the final D-forecasting model. The use of energy price data to predict the energy demand is an important contribution given the relationships between these variables are well established. This objective resulted in the bivariate BB7 and BB8 copulas as being ranked highly for the probabilistic forecasting of D at a timescale of 30 minutes, 1-hour, and daily. The conditional exceedance probability of electricity demand greater than 7000 MW, 14000 MW, and 360000 MW for 30-minutes, 1-hour, and daily timescales given their respective prices greater than AU25/MWh,AU25/MWh, AU60/MWh, and AU165/MWhpredictedtobe20165/MWh predicted to be 20%, 30%, and 50% respectively. Similarly, the conditional non-exceedance probability of electricity demand greater than 7000 MW, 14000 MW, and 360000 MW for 30-minutes, 1-hour, and daily timescales given their respective prices greater than AU25/MWh, AU60/MWh,andAU60/MWh, and AU165/MWh was predicted to be 80%, 72%, and 70% respectively. When benchmarked with literature, the proposed research methodologies for objective (i.e., projection of demand based on antecedent behaviour) and objective 2 (i.e., projection of demand based on antecedent energy price data) appear to be versatile tools possessing a robust predictive capability for forecasting D in Queensland, Australia. Hence, this research project is the first to develop and test these novel techniques, especially using price as regression covariate to forecast demand to achieve high forecasting accuracy, when the models are applied for multiple forecasting horizons of 30-minutes, 1-hour, 6-hourly, 12-hourly, and daily. It is noted that these timescales are relevant for stakeholders (e.g., energy utilities) to develop decision systems for better energy security, and can potentially be adopted in real power grid operations to ensure stability, cost reduction and improved efficiency whilst granting consumer satisfaction. In summary, the novel energy demand modelling techniques presented here can help address research gaps in electricity usage monitoring sector by making a significant contribution towards improved forecasting accuracy of energy demand. While this study has currently been limited to Queensland, the research findings are immensely useful for energy experts in the National Energy Markets elsewhere including supporting the work of AEMO, Energex and other companies to enhance their energy forecasting and monitoring skills. These can assist in informed decisions and addressing the growing challenges within electricity industry, through improving energy demand and price monitoring, consumer satisfaction and maximized profitability endeavours of energy companies

    Short-term electricity price point and probabilistic forecasts

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    Accurate short-term electricity price forecasts are essential to all electricity market participants. Generation companies adopt price forecasts to hedge generation shortage risks; load serving entities use price forecasts to purchase energy with low cost; and trading companies utilize price forecasts to arbitrage between markets. Currently, researches on point forecast mainly focus on exploring periodic patterns of electricity price in time domain. However, frequency domain enables us to identify more information within price data to facilitate forecast. Besides, price spike forecast has not been fully studied in the existing works. Therefore, we propose a short-term electricity price forecast framework that analyzes price data in frequency domain and consider price spike predictions. First, the variational mode decomposition is adopted to decompose price data into multiple band-limited modes. Then, the extended discrete Fourier transform is used to transform the decomposed price mode into frequency domain and perform normal price forecasts. In addition, we utilize the enhanced structure preserving oversampling and synthetic minority oversampling technique to oversample price spike cases to improve price spike forecast accuracy. In addition to point forecasts, market participants also need probabilistic forecasts to quantify prediction uncertainties. However, there are several shortcomings within current researches. Although wide prediction intervals satisfy reliability requirement, the over-width intervals incur market participants to derive conservative decisions. Besides, although electricity price data follow heteroscedasticity distribution, to reduce computation burden, many researchers assume that price data follow normal distribution. Therefore, to handle the above-mentioned deficiencies, we propose an optimal prediction interval method. 1) By considering both reliability and sharpness, we ensure the prediction interval has a narrow width without sacrificing reliability. 2) To avoid distribution assumptions, we utilize the quantile regression to estimate the bounds of prediction intervals. 3) Exploiting the versatile abilities, the extreme learning machine method is adopted to forecast prediction intervals. The effectiveness of proposed point and probabilistic forecast methods are justified by using actual price data from various electricity markets. Comparing with the predictions derived from other researches, numerical results show that our methods could provide accurate and stable forecast results under different market situations

    Advanced Methods of Power Load Forecasting

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    This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load

    Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

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    The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

    Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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    More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy
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