Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey

Machine learning methods are increasingly widely used in high-risk settings such as healthcare, transportation, and finance. In these settings, it is important that a model produces calibrated uncertainty to reflect its own confidence and avoid failures. In this paper we survey recent works on uncertainty quantification (UQ) for deep learning, in particular distribution-free Conformal Prediction method for its mathematical properties and wide applicability. We will cover the theoretical guarantees of conformal methods, introduce techniques that improve calibration and efficiency for UQ in the context of spatiotemporal data, and discuss the role of UQ in the context of safe decision making

A review of probabilistic forecasting and prediction with machine learning

Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and methods have not been formalized and structured under a holistic view of the entire field. Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review covers a time period spanning from the introduction of early statistical (linear regression and time series models, based on Bayesian statistics or quantile regression) to recent machine learning algorithms (including generalized additive models for location, scale and shape, random forests, boosting and deep learning algorithms) that are more flexible by nature. The review of the progress in the field, expedites our understanding on how to develop new algorithms tailored to users' needs, since the latest advancements are based on some fundamental concepts applied to more complex algorithms. We conclude by classifying the material and discussing challenges that are becoming a hot topic of research.Comment: 83 pages, 5 figure

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

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

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 AU$25/MWh, AU$60/MWh, and AU$165/MWh predicted to be 20$25/MWh, AU$60/MWh, and AU$165/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

Retrospective Uncertainties for Deep Models using Vine Copulas

Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge. Existing solutions rely on modified loss functions or architectural changes. We propose to compensate for the lack of built-in uncertainty estimates by supplementing any network, retrospectively, with a subsequent vine copula model, in an overall compound we call Vine-Copula Neural Network (VCNN). Through synthetic and real-data experiments, we show that VCNNs could be task (regression/classification) and architecture (recurrent, fully connected) agnostic while providing reliable and better-calibrated uncertainty estimates, comparable to state-of-the-art built-in uncertainty solutions.The research leading to these results has received funding from the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement num. 101069595 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773). Additionally, this work has been partially supported by Grant PID2019-107255GB-C21 funded by MCIN/AEI/ 10.13039/501100011033.Peer ReviewedPostprint (published version

Applications of Probabilistic Forecasting in Smart Grids : A Review

This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area