Prior Day Effect in Forecasting Daily Natural Gas Flow from Monthly Data

Many needs exist in the energy industry where measurement is monthly yet daily values are required. The process of disaggregation of low frequency measurement to higher frequency values has been presented in this literature. Also, a novel method that accounts for prior-day weather impacts in the disaggregation process is presented, even though prior-day impacts are not directly recoverable from monthly data. Having initial daily weather and gas flow data, the weather and flow data are aggregated to generate simulated monthly weather and consumption data. Linear regression models can be powerful tools for parametrization of monthly/daily consumption models and will enable accurate disaggregation. Two-, three-, four-, and six-parameter linear regression models are built. RMSE and MAPE are used as means for assessing the performance of the proposed approach. Extensive comparisons between the monthly/daily gas consumption forecasts show higher accuracy of the results when the effect of prior-day weather inputs are considered

Remote sensing for detection of soil limitations in agricultural areas Quarterly report, 1 Apr. - 1 Jul. 1970

Remote sensing for detecting soil limitations in agricultural area

Machine Learning in Minecraft: Proof of Concept for Object Detection Oriented Autonomous Bots in Minecraft

Machine learning provides new methods of problem solving through applied pattern recognition. An interesting challenge is to utilize machine learning in the automation of tasks and behaviors in virtual environments. Minecraft is an open-world, sandbox style game giving players nearly limitless freedom to alter a procedurally generated world. In the survival game mode, the player must collect resources to craft tools and build structures. The collection of resources can be tedious, so this project seeks to automate the standard initial task of collecting wood. By combining a convolutional neural network with API, a bot can collect resources while remaining scalable to procedural environments. This project utilizes the API Mineflayer for movement, and the Yolov8 neural network architecture from the Ultralytics package in Python for object detection. Data was initially collected in the form of 512x288 pixel images, uniformly sampled from the student researchers’ Minecraft gameplay. The data were then scaled and manually labeled into 7 distinct classes for each type of tree. After training the neural network for 15 epochs, the network could detect trees with an average precision of 88.5% at a recall threshold of 50%. This project has several limitations. Currently, the project is designed only to work on locally hosted servers. Furthermore, the bot’s point of view is generated by a simplified render of the Minecraft environment without dynamic lighting. Lastly, the difficulty is restricted so that the bot only encounters environmental threats. Future researchers may take interest in addressing any of these limitations or may create new datasets and scripts for collecting different resources. This project can also be expanded to include a state machine that switches between neural networks and scripts to carry out more complex behaviors as a sequence of discrete tasks