Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning

Høverstad, Boye Annfelt; Tidemann, Axel; Langseth, Helge; Öztürk, Pınar

doi:10.1109/tsg.2015.2395822

Cited by 77 publications

(36 citation statements)

References 34 publications

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“…Consequently, the effect of temperature must be included in the short-term load forecasting. The different eight day categories are enumerated in [30].…”

Section: Related Workmentioning

confidence: 99%

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

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Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.

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“…Consequently, the effect of temperature must be included in the short-term load forecasting. The different eight day categories are enumerated in [30].…”

Section: Related Workmentioning

confidence: 99%

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

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“…Tao's Vanilla Benchmark model is a frequently-cited regression model for load forecasting. It was used in GEFCom2012 as the benchmark model [7], and then reproduced by other scholars [21,22]. The model is specified as in Equation (1):…”

Section: Multiple Linear Regression Models For Load Forecastingmentioning

confidence: 99%

Wind Speed for Load Forecasting Models

Xie

Hong

2017

Sustainability

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Temperature and its variants, such as polynomials and lags, have been the most frequently-used weather variables in load forecasting models. Some of the well-known secondary driving factors of electricity demand include wind speed and cloud cover. Due to the increasing penetration of distributed energy resources, the net load is more and more affected by these non-temperature weather factors. This paper fills a gap and need in the load forecasting literature by presenting a formal study on the role of wind variables in load forecasting models. We propose a systematic approach to include wind variables in a regression analysis framework. In addition to the Wind Chill Index (WCI), which is a predefined function of wind speed and temperature, we also investigate other combinations of wind speed and temperature variables. The case study is conducted for the eight load zones and the total load of ISO New England. The proposed models with the recommended wind speed variables outperform Tao's Vanilla Benchmark model and three recency effect models on four forecast horizons, namely, day-ahead, week-ahead, month-ahead, and year-ahead. They also outperform two WCI-based models for most cases.

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“…In the bottom-up method, the terminal appliance is the study object and the focus is on the consumption model formulation of each terminal appliance [5,6]; while in the statistical method, the study object is to describe the characteristics of the input data based on a set of measured load profiles, and then a prediction model is formulated according to the extracted character [7,8]. Obviously, although the bottom-up method requires a large amount of data that can reflect the consumption behavior of household appliances, it has a high prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

2018

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Abstract:The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances' switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach.

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Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning

Cited by 77 publications

References 34 publications

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Wind Speed for Load Forecasting Models

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

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