Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction

• URL: http://arxiv.org/abs/2207.01019v1
• Date: Sun, 3 Jul 2022 12:16:54 GMT
• Title: Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction
• Authors: Muhammad Bilal, Hyeok Kim, Muhammad Fayaz, Pravin Pawar
• Abstract summary: We use Weka, a data mining tool, to first apply models on hourly and daily household energy consumption datasets available from Kaggle data science community. Secondly, we also implemented time series forecasting models, ARIMA and VAR, in python to forecast household energy consumption of selected South Korean households with and without weather data. Our results show that the best methods for the forecasting of energy consumption prediction are Support Vector Regression followed by Multilayer Perceptron and Gaussian Process Regression.
• Score: 3.7458346891274013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a result of increasing population and globalization, the demand for energy has greatly risen. Therefore, accurate energy consumption forecasting has become an essential prerequisite for government planning, reducing power wastage and stable operation of the energy management system. In this work we present a comparative analysis of major machine learning models for time series forecasting of household energy consumption. Specifically, we use Weka, a data mining tool to first apply models on hourly and daily household energy consumption datasets available from Kaggle data science community. The models applied are: Multilayer Perceptron, K Nearest Neighbor regression, Support Vector Regression, Linear Regression, and Gaussian Processes. Secondly, we also implemented time series forecasting models, ARIMA and VAR, in python to forecast household energy consumption of selected South Korean households with and without weather data. Our results show that the best methods for the forecasting of energy consumption prediction are Support Vector Regression followed by Multilayer Perceptron and Gaussian Process Regression.

Related papers

• AI-Powered Predictions for Electricity Load in Prosumer Communities [0.0]
  We present and test artificial intelligence powered short-term load forecasting methodologies. Results show that the combination of persistent and regression terms (adapted to the load forecasting task) achieves the best forecast accuracy.
  arXiv  Detail & Related papers  (2024-02-21T12:23:09Z)
• Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System [4.573008040057806]
  We analyze how transfer learning can help by exploiting data from multiple households to improve a single house's load forecasting. Specifically, we train an advanced forecasting model using data from multiple different households, and then finetune this global model on a new household with limited data. The obtained models are used for forecasting power consumption of the household for the next 24 hours(day-ahead) at a time resolution of 15 minutes.
  arXiv  Detail & Related papers  (2023-10-29T21:19:08Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine [0.0]
  The proposed methodology was constructed using hourly data from Ukraine's electricity consumption ranging from 2013 to 2020. Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution.
  arXiv  Detail & Related papers  (2023-04-11T12:15:50Z)
• GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
  We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
  arXiv  Detail & Related papers  (2022-12-24T18:15:39Z)
• A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting [0.0]
  In this study, a modified discrete particle swarm optimization (MDPSO) was employed for feature selection. Compared with other well-established counterparts, MDPSO-SVR model consistently performs best in two real-world electricity consumption datasets.
  arXiv  Detail & Related papers  (2022-06-14T07:50:04Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• 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 consumption forecasting using a stacked nonparametric Bayesian approach [3.4449150144113254]
  We study the process of forecasting household energy consumption using multiple short time series data. We construct a stacked GP method where the predictive posteriors of each GP applied to each task are used in the prior and likelihood of the next level GP. We apply our model to a real-world dataset to forecast energy consumption in Australian households across several states.
  arXiv  Detail & Related papers  (2020-11-11T02:27:00Z)
• Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids [67.72249211312723]
  Real-time state estimation and forecasting is critical for efficient operation of power grids. PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
  arXiv  Detail & Related papers  (2020-10-09T14:18:31Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

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.