Predicting Solar Heat Production to Optimize Renewable Energy Usage

• URL: http://arxiv.org/abs/2405.09972v1
• Date: Thu, 16 May 2024 10:32:39 GMT
• Title: Predicting Solar Heat Production to Optimize Renewable Energy Usage
• Authors: Tatiana Boura, Natalia Koliou, George Meramveliotakis, Stasinos Konstantopoulos, George Kosmadakis,
• Abstract summary: We present an approach that uses machine learning to automatically construct and continuously adapt a model that predicts heat production. We present positive empirical results for the predictive accuracy of our solution, and discuss the impact of these results on the end-to-end system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Utilizing solar energy to meet space heating and domestic hot water demand is very efficient (in terms of environmental footprint as well as cost), but in order to ensure that user demand is entirely covered throughout the year needs to be complemented with auxiliary heating systems, typically boilers and heat pumps. Naturally, the optimal control of such a system depends on an accurate prediction of solar thermal production. Experimental testing and physics-based numerical models are used to find a collector's performance curve - the mapping from solar radiation and other external conditions to heat production - but this curve changes over time once the collector is exposed to outdoor conditions. In order to deploy advanced control strategies in small domestic installations, we present an approach that uses machine learning to automatically construct and continuously adapt a model that predicts heat production. Our design is driven by the need to (a) construct and adapt models using supervision that can be extracted from low-cost instrumentation, avoiding extreme accuracy and reliability requirements; and (b) at inference time, use inputs that are typically provided in publicly available weather forecasts. Recent developments in attention-based machine learning, as well as careful adaptation of the training setup to the specifics of the task, have allowed us to design a machine learning-based solution that covers our requirements. We present positive empirical results for the predictive accuracy of our solution, and discuss the impact of these results on the end-to-end system.

Related papers

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning [0.0]
  We develop statistical and machine learning methods for post-processing ensemble predictions of global horizontal irradiance and solar power generation. Our results indicate that post-processing substantially improves the solar power generation forecasts, in particular when post-processing is applied to the power predictions.
  arXiv  Detail & Related papers  (2024-06-06T18:08:50Z)
• Advanced simulation-based predictive modelling for solar irradiance sensor farms [0.5292801941204784]
  This work introduces a novel framework named Cloud-based Analysis and Integration for Data Efficiency (CAIDE) CAIDE is designed for real-time monitoring, management, and forecasting of solar irradiance sensor farms. The framework has important implications for the deployment of solar plants and the future of renewable energy sources.
  arXiv  Detail & Related papers  (2024-04-05T15:44:51Z)
• Global Transformer Architecture for Indoor Room Temperature Forecasting [49.32130498861987]
  This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings. It aims at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings.
  arXiv  Detail & Related papers  (2023-10-31T14:09:32Z)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic Buildings [0.0]
  This work presents an interpretable machine learning model aimed at predicting room temperature in non-domestic buildings. We demonstrate experimentally that the proposed model can accurately forecast room temperatures eight hours ahead in real-time.
  arXiv  Detail & Related papers  (2021-11-23T11:16:35Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)
• Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space [62.39318039798564]
  We propose a scheduling mechanism capable of taking advantage of correlated information. The proposed mechanism is capable of determining the frequency with which sensors should transmit their updates. We show that our solution can significantly extend the sensors' lifetime.
  arXiv  Detail & Related papers  (2020-11-19T09:53:27Z)
• Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models [14.579720180539136]
  We train and evaluate the models using public data from seven stations in the SURFRAD network. We show that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model.
  arXiv  Detail & Related papers  (2020-10-09T17:57:59Z)
• Simulation and Optimisation of Air Conditioning Systems using Machine Learning [4.511561231517167]
  In building management, usually static thermal setpoints are used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy. This paper explores how to optimise the setpoints used in a particular room during its unoccupied periods using machine learning approaches.
  arXiv  Detail & Related papers  (2020-06-27T06:42:25Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.