Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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

Development and Application of Artificial Neural Networks for Energy Demand Forecasting in Australia

Energy plays a very significant role in the operation of the economic machinery of a country. Insufficient energy supply can lead to high energy prices, which, in turn, can cause many, if not all, prices of commodities to increase. This leads to inflation and all its consequential adverse outcomes within the economy. For this reason, energy planning is an essential macroeconomic planning activity for the economic planning of the country. Like most other countries, energy planning in Australia is done through econo metric modelling. This is done using linear regression models that correlate energy demand (independent variable) to other macroeconomic factors, like gross domes tic product, as dependent variables. However, such a technique is unsuitable when complex interactions exist between variables. Motivated by the success of Artificial Neural Networks (ANN), this thesis aims to develop an ANN model as an alternative tool for energy demand forecasting in Australia. First, a set of macroeconomic variables is chosen as potential input features (independent variables) for the energy demand forecasting model. The output feature (dependent variable) is the monthly energy use of Australia. The output feature was measured in tons of oil. Several incremental models are developed and run to see the incremental effect of adding features on the accuracy of performance of the ANN models. Correlation analysis of features is also performed to see how each feature affects the output feature and how they are related. Then, to determine what structure of an ANN would provide better perfor mance, three different structures are chosen and run on different datasets. Dif ferent testing and training periods are used to establish the performance of each model. To automate the process of optimising ANN models, a genetic algorithm is designed to optimise the number of neurons in different ANNs. In doing this, the overall structures and performance of optimised ANNs for predicting energy production in the Australian context are presented and assessed. Also, different types of evolutionary operators are designed and tested. The results of all variants are analysed, showing the benefit of optimising the ANN structure

Design of Smart Energy Generation and Demand Response System in Saudi Arabia

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThough the promising benefits of renewable sources have already pushed many countries into implementing RE units, Saudi Arabia is still highly dependent on fossil fuel. However, the decrease in value of oil reserves has enforced Saudi Arabia to prioritize renewable energy sources in the next decades. Such energy sources are highly dependable on accurate forecasts, due to their intermittence and operability. The present research has the objective to develop models that can accurately forecast energy load for implementation in a decision-making system. The case investigated is the western region of Saudi Arabia. Two modelling approaches were evaluated, linear regression (LR) and artificial neural network (ANN). This last one was chosen because it is a mathematical model able to deal with non-linear relationship among input(s) and output(s) in the data. Time series (past load data) and multivariate data from 2010 until 2016 were investigated A hybrid model structure (combiner) was implemented to analyse the effects of combining outputs of two models in a single one. This hybrid model consisted of a regular average and weighted average of the time series and multivariate model, with calibration through Fuzzy and Particle Swarm Optimisation. These two were selected because, while Particle Swarm Optimization is an optimization algorithm, Fuzzy consists in a complete structured model. The forecasted load and the available input were used in the last chapter for power generation planning and decision-making support. The software used for the modelling and simulation is ETAP®. Different scenarios for replacement of fossil fuel power plants by renewable units were tested considering the network of western Saudi Arabia. The results show that Artificial Neural Network with time series input and 15 neurons in hidden layer shows superior performance (MSE 3.7*105 and R2 equals 99%) compared to other neural networks and linear regression. Though the application of combiner models did not significantly improve model performance, the Fuzzy Combiner shows the best one (MSE 5.8*105 and R2 equals 93%) since it incorporates information from time series and multivariate data. It is important to mention that all the modelling approaches evaluated have some limitations, such as the necessity of accurate input data and they are limited in capability of extrapolating over the training range. In the last section, it was observed that renewable energy sources can be integrated in the grid network without excessive risk regarding demand. This occurs because the current energy management policy of western Saudi Arabia enables the use of energy units with fast compensation (using gas units) in the case of demand increase or decrease in solar or wind power

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or win