Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

Li, Yuancheng; Guo, Panpan; Li, Xiang

doi:10.3390/a9040080

Cited by 25 publications

(17 citation statements)

References 28 publications

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“…Therefore, the forecast on power load has become an important research focus and has been investigated continuously in recent years, from which power suppliers and consumers can benefit to develop better energy management. The forecasting period ranges from minutes to years due to the various demands for power load forecasting [5][6][7]. Many models have been developed to address this problem, and most of them can be classified into three categories: regression models, time series analysis, and artificial neural networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

2018

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Abstract:The energy management of office buildings has been a rising concern for owners, researchers, and energy suppliers. The volatility of power load in office buildings threatens energy consumption and risks device security. This paper investigates the load fluctuation patterns in an office building based on user data, using recurrence interval analysis for different thresholds. The recurrence intervals of volatility are fitted by stretched exponential distribution, from which the probability density function is derived. Then, the short-term and long-term memory effect on the fluctuations are learned by conditional probability density function and multifractal detrended fluctuation analysis, respectively. A hazard function is further established to analyze the risk estimation of load volatility and derive the value at risk (VaR). Thus, a functional relationship has been established between average recurrence interval and threshold. The methodology and analysis results addressed in this paper help to understand load fluctuation patterns and aid in the design of energy consumption strategies in office buildings. According to the results of our research, conclusions and management suggestions are provided at the end of this paper.

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Section: Introductionmentioning

confidence: 99%

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

2018

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“…Other developed methods for short-term load forecasts, like [9,17], also depend on measurements and do not work at all in such cases. These methods would distribute no valid forecasts at all.…”

Section: Improvement Of the Load Forecastmentioning

confidence: 99%

Evaluation of Different Development Possibilities of Distribution Grid State Forecasts

et al. 2020

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The number of renewable energy systems is still increasing. To reduce the worldwide CO2 emissions, there will be even more challenges in the distribution grids like currently upcoming charging stations or heat pumps. All these new electric systems in the low voltage (LV) and medium voltage (MV) levels are characterized by an unsteady behavior. To monitor and predict the behavior of these new flexible systems, a grid state forecast is needed. This software tool calculates wind, photovoltaic, and load forecasts. These power forecasts are already in the focus of research, but there are some specific use cases, which require a more specific solution. To get a variously applicable software tool, different new functions to improve an already existing grid state forecast tool were developed and evaluated. For example, it will be proofed if a grid state forecast tool can be improved by calculating the number or the base load of the loads in grid areas by just one available measurement. Another big subject exists in the exchange of forecast information between different voltage levels. How this can be realized and how big the effect on the forecast quality is, will be analyzed. The results of these evaluations will be shown in this paper.

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“…were all confirmed to be relevant to electricity load forecast by regression analyses. In the 21st century, the emergence of smart technology could help researchers grab high-frequency data from a personal level and add human behavior into a regression model to improve the forecast accuracy [26,27]. Briefly, regression analysis aimed to describe the quantitative relationship between the observed variables in statistics, however, it could not capture the spatiotemporal variation and is sometimes restricted by the data volume.…”

Section: Literature Reviewmentioning

confidence: 99%

The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China

Zhang

2018

Energies

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Abstract:The significant fluctuation of industrial electricity consumption has a high impact on power load, which makes the research on recurrence intervals between extreme events of theoretical and practical significance. The study uses a high-frequency data of heavy and light industries and employs recurrence interval analysis in different thresholds. We find that the reoccurrence interval of volatility can fit with the stretched exponential function and the probability density functions of recurrence intervals in various thresholds shows a scaling behavior. Then, the conditional probability density function and the multifractal detrended fluctuation analysis demonstrate the existence of short-range correlation, long-range correlation, and multifractal properties, respectively. We further construct a hazard function, introduce recurrence intervals into VaR calculation and establish a functional relationship between average recurrence interval and threshold. Following this result, we also shed light on policy discussion for multi-industrial electricity supply management.

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Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

Cited by 25 publications

References 28 publications

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

Evaluation of Different Development Possibilities of Distribution Grid State Forecasts

The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China

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