Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.Comment: 9 pages, Presented at 2018 INFORMS Conference on Business Analytics and Operations Research (Baltimore, MD, USA). One of the winning solutions to the 2018 Syngenta Crop Challeng

A Protocol to Build Trust with Black Box Models

Data scientists are more widely using artificial intelligence and machine learning (ML) algorithms today despite the general mistrust associated with them due to the lack of contextual understanding of the domain occurring within the algorithm. Of the many types of ML algorithms, those that use non-linear activation functions are especially regarded with suspicion because of the lack of transparency and intuitive understanding of what is occurring within the black box of the algorithm. In this thesis, we set out to create a protocol to delve into the black box of an ML algorithm set to predict synoptic severe weather patterns and discover if we can more closely observe what is occurring inside the algorithm. In doing so, we prove that despite the lack of context considered when creating the algorithm there can be some recognition of key synoptic features. This protocol is aided by the introduction of a novel visualization tool that acts to peer inside the hidden nodes of an artificial neural network to better diagnose the black box. To show that this protocol and tool have merit, we also consider 5 generalized questions that should be answered to develop trust with ML algorithms

Feature-based time-series analysis

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.Comment: 28 pages, 9 figure

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

An IoT-based Thermal Modelling of Dwelling Rooms to Enable Flexible Energy Management

The thermal model of dwellings is the basis for flexible energy management of smart homes, where heating load is a big part of demand. It can also be operated as virtual energy storage to enable flexibility. However, constrained by data measurements and learning methods, the accuracy of existing thermal models is unsatisfying due to time-varying disturbances. This paper, based on the edge computing system, develops a dark-grey box method for dwelling thermal modelling. This darkgrey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated on a real room, enabled by an Internet of Things (IoT) platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights in selecting appropriate feature-learning methods in practice. This work provides more accurate thermal modelling, thus enabling more efficient energy use and management and helping reduce energy bills

An IoT-based Thermal Modelling of Dwelling Rooms to Enable Flexible Energy Management

The thermal model of dwellings is the basis for flexible energy management of smart homes, where heating load is a big part of demand. It can also be operated as virtual energy storage to enable flexibility. However, constrained by data measurements and learning methods, the accuracy of existing thermal models is unsatisfying due to time-varying disturbances. This paper, based on the edge computing system, develops a dark-grey box method for dwelling thermal modelling. This darkgrey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated on a real room, enabled by an Internet of Things (IoT) platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights in selecting appropriate feature-learning methods in practice. This work provides more accurate thermal modelling, thus enabling more efficient energy use and management and helping reduce energy bills