Permutation-Based Residential Short-term Load Forecasting in the Context of Energy Management Optimization Objectives

Voß, Marcus

doi:10.1145/3396851.3397731

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Furthermore, considering the phase error, the point-wise metrics, such as MAPE, RMSE, and MSE, are claimed to be inappropriate in time-series prediction evaluation [5], [16], [17], [29]. Point-wise metrics simply compare the observed and predicted values at each time step, and hence, they lead to double penalty effect (DPE).…”

Section: Problem Statementmentioning

confidence: 99%

“…Based on the test they run, they find that the new metric they proposed is suitable and useful for volatile and irregular data while the standard point-wise metrics are adequate for smooth and regular data. Similarly, alignment-based metrics such as Dynamic Time Warping, Longest Common Sequence, Parameterised Forecast Error Metric, and Move Split Merge are also proposed as evaluation metrics for time-series predictions [5], [16]. Alignment-based metrics mainly align the predictions with the actual values in order to find the optimal match between them, hence they do prevent the DPE.…”

Section: Problem Statementmentioning

confidence: 99%

“…In most countries, household electricity consumption accounts for a significant proportion of the overall demand [2], [3]; and many opportunities remain for efficiency improvements and alleviation of the volatile load that the households bring into the power systems [4]. To this end, households are increasingly equipped with smart sensors, storage devices, and distributed and renewable energy generators to optimise consumption and minimise waste through intelligent and automatic energy management systems [5], [6]. Such systems, which enable household energy users to better manage their For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising.…”

Section: Introductionmentioning

confidence: 99%

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Avoiding Overconfidence in Predictions of Residential Energy Demand Through Identification of the Persistence Forecast Effect

Akyol

Preist

Schien

2023

IEEE Trans. Smart Grid

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Forecasting domestic electricity consumption is important for a wide range of modern power system solutions and smart applications that support network operation, grid stability, and demand-side management, most of which depend on robust and accurate predictions. The methods producing these predictions infer future load from statistical regularity in historical data. If such regularity is lacking, predictions then regress towards the most recently observed consumption value used in the input set. Predictions then follow the actual load data one step behind in time, potentially affecting the robustness of predictions and functionality of applications. Current evaluation methods do not detect this behaviour which may result in overconfidence in prediction results. In this study, we I) define and systematically analyse this behaviour, which we label the Persistence Forecast Effect and illustrate its impacts, II) propose a novel method, called 1-Step-Shifting, to detect its presence, and III) analyse and establish the relationship between irregularity in data and the effect. Further, we provide a case study applying state-of-the-art forecasting techniques to a real-world dataset of electricity consumption data from 69 households in order to demonstrate the Persistence Forecast Effect, its implications, and its relationship to statistical regularity in historical data.

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Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Avoiding Overconfidence in Predictions of Residential Energy Demand Through Identification of the Persistence Forecast Effect

Akyol

Preist

Schien

2023

IEEE Trans. Smart Grid

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“…Rowe et al [186] show how the AAF can be used in a peak reduction algorithm for battery control in LV networks. Voss [214] showed how optimal choice and configuration of the error measure, depends on the specific down-stream optimization objective for household-level energy management. More studies are needed in these specific downstream applications.…”

Section: Forecast Evaluationmentioning

confidence: 99%

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

Haben,

Arora,

Giasemidis

et al. 2021

Preprint

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The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage (LV), localised networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known LV level open datasets to encourage further research and development.

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Review of low voltage load forecasting: Methods, applications, and recommendations

et al. 2021

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Permutation-Based Residential Short-term Load Forecasting in the Context of Energy Management Optimization Objectives

Cited by 4 publications

References 7 publications

Avoiding Overconfidence in Predictions of Residential Energy Demand Through Identification of the Persistence Forecast Effect

Avoiding Overconfidence in Predictions of Residential Energy Demand Through Identification of the Persistence Forecast Effect

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

Review of low voltage load forecasting: Methods, applications, and recommendations

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