Multistep energy consumption forecasting by metaheuristic optimization of time‐series analysis and machine learning

Chou, Jui‐Sheng; Truong, Dinh‐Nhat

doi:10.1002/er.6125

Cited by 45 publications

(29 citation statements)

References 49 publications

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“…Chou and Truong [54] proposed a hybrid system composed of four steps: linear time series modeling, non-linear residual modeling, combination, and optimization. The parameter selection process for the models employed in the first three steps is performed through a Jellyfish Search (JS) optimization algorithm [55].…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

et al. 2021

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The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the linear and seasonal component of the time series using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, (ii) forecast of the error series using an ML technique, and (iii) combination of both linear and non-linear forecasts from (i) and (ii) using a a secondary ML model. EvoHyS employs a Genetic Algorithm (GA) for feature selection and hyperparameter optimization in phases (ii) and (iii) aiming to improve its accuracy. An experimental evaluation was conducted using consumption energy data of a smart grid in a one-step-ahead scenario. The proposed hybrid system reaches statistically significant improvements when compared to other statistical, hybrid, and ML approaches from the literature utilizing well known metrics, such as Mean Squared Error (MSE).

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Section: Related Workmentioning

confidence: 99%

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

et al. 2021

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“…In [50], the authors developed a forecasting system that optimises linear time series (using linear time-series model, a Seasonal Auto-Regressive Integrated Moving Average) with non-linear ML models (least squares support vector regression model) in order to identify the historical pattern of energy consumption and to predict multi-step ahead energy consumption. Optimisation algorithms were investigated using high-dimension mathematical benchmark functions, and computational time and input needs were assessed.…”

Section: Forecasting Of Energy Consumption In Buildingsmentioning

confidence: 99%

Design of Ensemble Forecasting Models for Home Energy Management Systems

et al. 2021

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The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

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“…This technique is used to keep the energy demand and supply in balance and to effectively dispatch the energy capacity. 30 Machine learning models are widely used to analyze the load/demand forecasting. Time series analysis is done to identify the appropriate lags and seasonal patterns for load forecasting.…”

Section: Load and Demand Forecastingmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An indepth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

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Multistep energy consumption forecasting by metaheuristic optimization of time‐series analysis and machine learning

Cited by 45 publications

References 49 publications

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Design of Ensemble Forecasting Models for Home Energy Management Systems

A holistic review on energy forecasting using big data and deep learning models

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