Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

Decarbonization cost of Bangladesh's energy sector: Influence of corruption

As a rapidly developing lower-middle income country, Bangladesh has been maintaining a steady growth of +5% in the gross domestic product (GDP) annually since 2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle- income and developed by 2021 and 2041 respectively, which translates to an annual GDP growth rate of 7.58% during this period. The bulk of this growth is expected to come from the manufacturing sector, the significant shift towards which started at the turn of this century. Energy intensity of manufacturing-based growth is higher, the evidence of which can be seen in the 3.17 times increase in national energy consumption between 2001 and 2014. Also, Bangladesh aims to achieve 100% electrification rate by 2021 against an annual population growth rate of 1.08%. With the increasing per capita income, there is now a growing middle class fuelling the growth in demand for convenient forms of energy. Considering the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher energy demand than that of 2010 by 2050. The government and private sector have started a substantial amount of investments in the energy sector to meet the signi ficant future demand. Approximately US$104 billion would be invested in the power sector of Bangladesh for establishing 33 GW installed capacity by 2030, the majority of which would be financed by national and international loans. However, Bangladesh is one of the most corrupted country in the world which may influence the energy planning development. The current policies of Bangladesh power sector paved the future direction towards predominantly coal-based energy mix which would augment the greenhouse gas (GHG) emissions five times (117.5 MtCO2e) in 2030 than that of 2010. By increasing GHG emissions, the country would undermine the worldwide effort of keeping global temperature rise in 21st century below 2°C, as per the Paris agreement and COP21. V The objective of this research was to develop a framework to explore the cost of decarbonizing the Bangladesh's energy sector by 2050. For the study, six emissions scenarios business as usual (BAU), current policy (CPS), high-carbon (HCS), medium-carbon (MCS), low-carbon (LCS) and zero-carbon scenarios (ZCS), and three economic conditions high, average and low costwere considered. The combination of emissions and economic scenarios rendered 18 different emissionseconomic scenarios for the research. The results showed that Bangladesh would emit 343 MtCO2e by 2050 without any emissions reduction strategies under HCS. However, Bangladesh can reduce 23 of HCS by adopting decarbonization strategies such as energy mix change towards renewable and nuclear. On the optimistic side, the emissions can be reduced 73 by 2050 under ZCS than that of HCS. The study demonstrated that a zero carbon future is not yet feasible for Bangladesh by 2050 because the operational fossil fuel based plants would be operational. Therefore, the GHG emissions are going to rise even if Bangladesh adopts renewables and nuclear dominating energy mix. However, it will be possible to keep the GHG emissions approximately 2 tCO2e/capita threshold if the country adopts LCS. On the other hand, only MCS and LCS can meet the projected energy demand by 2050. The energy sector can meet the projected demand under ZCS only if the electricity consumption is reduced 26 2050. In terms total cost, the MCS was found to be 3.9 LCS by 2050. LCS would have a higher cost than that of MCS up to 2030, due to the high capital cost of renewable technologies. The total cost under LCS would start to be lower than of MCS after 2035 for the fossil fuel cost. Accumulated fuel cost would reach$250 billion in 2050 under HCS, which can be reduced 23% under ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh is under rapid development, the accumulated capital would be comparatively high by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which would also ensure lower emissions. The study suggested that energy mix change, technological maturity, corruption and demand reduction can influence the cost of decarbonization. However, the most significant influencer for the decarbonization of Bangladeshi energy sector would be the corruption. Results showed that if Bangladesh can minimize the effect of corruption on the energy sector, it can reduce the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS

Evolutionary multivariate time series prediction

Multivariate time series (MTS) prediction plays a significant role in many practical data mining applications, such as finance, energy supply, and medical care domains. Over the years, various prediction models have been developed to obtain robust and accurate prediction. However, this is not an easy task by considering a variety of key challenges. First, not all channels (each channel represents one time series) are informative (channel selection). Considering the complexity of each selected time series, it is difficult to predefine a time window used for inputs. Second, since the selected time series may come from cross domains collected with different devices, they may require different feature extraction techniques by considering suitable parameters to extract meaningful features (feature extraction), which influences the selection and configuration of the predictor, i.e., prediction (configuration). The challenge arising from channel selection, feature extraction, and prediction (configuration) is to perform them jointly to improve prediction performance. Third, we resort to ensemble learning to solve the MTS prediction problem composed of the previously mentioned operations,  where the challenge is to obtain a set of models satisfied both accurate and diversity. Each of these challenges leads to an NP-hard combinatorial optimization problem, which is impossible to be solved using the traditional methods since it is non-differentiable. Evolutionary algorithm (EA), as an efficient metaheuristic stochastic search technique, which is highly competent to solve complex combinatorial optimization problems having mixed types of decision variables, may provide an effective way to address the challenges arising from MTS prediction. The main contributions are supported by the following investigations. First, we propose a discrete evolutionary model, which mainly focuses on seeking the influential subset of channels of MTS and the optimal time windows for each of the selected channels for the MTS prediction task. A comprehensively experimental study on a real-world electricity consumption data with auxiliary environmental factors demonstrates the efficiency and effectiveness of the proposed method in searching for the informative time series and respective time windows and parameters in a predictor in comparison to the result obtained through enumeration. Subsequently, we define the basic MTS prediction pipeline containing channel selection, feature extraction, and prediction (configuration). To perform these key operations, we propose an evolutionary model construction (EMC) framework to seek the optimal subset of channels of MTS, suitable feature extraction methods and respective time windows applied to the selected channels, and parameter settings in the predictor simultaneously for the best prediction performance. To implement EMC, a two-step EA is proposed, where the first step EA mainly focuses on channel selection while in the second step, a specially designed EA works on feature extraction and prediction (configuration). A real-world electricity data with exogenous environmental information is used and the whole dataset is split into another two datasets according to holiday and nonholiday events. The performance of EMC is demonstrated on all three datasets in comparison to hybrid models and some existing methods. Then, based on the prediction pipeline defined previously, we propose an evolutionary multi-objective ensemble learning model (EMOEL) by employing multi-objective evolutionary algorithm (MOEA) subjected to two conflicting objectives, i.e., accuracy and model diversity. MOEA leads to a pareto front (PF) composed of non-dominated optimal solutions, where each of them represents the optimal subset of the selected channels, the selected feature extraction methods and the selected time windows, and the selected parameters in the predictor. To boost ultimate prediction accuracy, the models with respect to these optimal solutions are linearly combined with combination coefficients being optimized via a single-objective task-oriented EA. The superiority of EMOEL is identified on electricity consumption data with climate information in comparison to several state-of-the-art models. We also propose a multi-resolution selective ensemble learning model, where multiple resolutions are constructed from the minimal granularity using statistics. At the current time stamp, the preceding time series data is sampled at different time intervals (i.e., resolutions) to constitute the time windows. For each resolution, multiple base learners with different parameters are first trained. Feature selection technique is applied to search for the optimal set of trained base learners and least square regression is used to combine them. The performance of the proposed ensemble model is verified on the electricity consumption data for the next-step and next-day prediction. Finally, based on EMOEL and multi-resolution, instead of only combining the models generated from each PF, we propose an evolutionary ensemble learning (EEL) framework, where multiple PFs are aggregated to produce a composite PF (CPF) after removing the same solutions in PFs and being sorted into different levels of non-dominated fronts (NDFs). Feature selection techniques are applied to exploit the optimal subset of models in level-accumulated NDF and least square is used to combine the selected models. The performance of EEL that chooses three different predictors as base learners is evaluated by the comprehensive analysis of the parameter sensitivity. The superiority of EEL is demonstrated in comparison to the best result from single-objective EA and the best individual from the PF, and several state-of-the-art models across electricity consumption and air quality datasets, both of which use the environmental factors from other domains as the auxiliary factors. In summary, this thesis provides studies on how to build efficient and effective models for MTS prediction. The built frameworks investigate the influential factors, consider the pipeline composed of channel selection, feature extraction, and prediction (configuration) simultaneously, and keep good generalization and accuracy across different applications. The proposed algorithms to implement the frameworks use techniques from evolutionary computation (single-objective EA and MOEA), machine learning and data mining areas. We believe that this research provides a significant step towards constructing robust and accurate models for solving MTS prediction problems. In addition, with the case study on electricity consumption prediction, it will contribute to helping decision-makers in determining the trend of future energy consumption for scheduling and planning of the operations of the energy supply system

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Recycled Materials in Civil and Environmental Engineering

The aim of this reprint was to report recent innovative studies based on the use of secondary raw materials for applications in civil and environmental engineering. To this purpose, papers were related to the preparation of innovative construction materials and to the treatment of wastes for environmental applications. The investigations were characterized by a common purpose, i.e., to find a way to reduce the amount of waste generated, thus reducing the need for landfilling and optimizing the values of these novel materials, which are an abundant resource that can be easily reused for different applications