Electricity consumption forecasting in Italy using linear regression models

Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio

doi:10.1016/j.energy.2009.06.034

Cited by 503 publications

(228 citation statements)

References 22 publications

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“…In addition to studies that focusing on the forecasting methodology, 9 researchers have also studied particular geographical areas or countries [2], [5], [6], [26], [29], [31]. Competitions have also been organized [9], [17].…”

Section: Related Workmentioning

confidence: 99%

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Wijaya

Vasirani

Humeau

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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Abstract-While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base.

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Section: Related Workmentioning

confidence: 99%

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Wijaya

Vasirani

Humeau

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

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“…Regression analysis is also held up as a suitable method for providing results in a format that are relatively simple to interpret for non-statistical audiences, for example local authority officers (Bianco et al 2009;Aranda et al 2012). The use of a regression model is a compromise between the simplicity of the evaluation method and the accuracy of the result without requiring a significant amount of input data (Aranda et al 2012).…”

Section: Statistical Modellingmentioning

confidence: 99%

Benchmarking and tracking domestic gas and electricity consumption at the local authority level

et al. 2015

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Government, local authority and industry initiatives to improve the energy efficiency of housing stocks are central to national and international commitments to reduce carbon dioxide emissions. To be effective, initiatives need to target homes which, given their location, size, fuel type and occupancy, use more energy than expected. This paper illustrates how energy efficiency benchmarks can be developed that account for these factors and highlights the shortcomings of relying on simple energy consumption statistics. The study uses existing data (with national coverage) and the measured electricity and gas consumption of groups consisting of, on average, 500-700 households to benchmark and track domestic gas and electricity consumption across England. Multiple regression models, which account for 65 % of the variation in domestic gas consumption and 73 % of domestic electricity variation, are used to derive the benchmarks. The actual gas and electricity consumption of each group of homes is compared against the derived benchmark and an energy efficiency index presented. The approach enables changes in energy efficiency to be tracked temporally, for example to assess the effectiveness of government, local authority or industry initiatives. National and city-scale patterns of energy efficiency are also discussed.

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“…The regression model is one of the main forecasting techniques [28]. These three methods are widely applied in industries that demand forecasting [15,25,6,7,20]. However, in the past three years, the LME model has been broadly applied in various fields, such as the timber industry [30], medicine [9,49], and ecology [31], and to identify crucial influential factors.…”

Section: Forecasting Methodologymentioning

confidence: 99%

Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

Liao

Chiu

2017

Croat. Oper. Res. Rev.

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Abstract. In the past few decades, demand forecasting has become relatively difficult due to rapid changes in the global environment. This research illustrates the use of the make-to-stock (MTS) production strategy in order to explain how forecasting plays an essential role in business management. The linear mixed-effect (LME) model has been extensively developed and is widely applied in various fields. However, no study has used the LME model for business forecasting. We suggest that the LME model be used as a tool for prediction and to overcome environment complexity. The data analysis is based on real data in an international display company, where the company needs accurate demand forecasting before adopting a MTS strategy. The forecasting result from the LME model is compared to the commonly used approaches, including the regression model, autoregressive model, times series model, and exponential smoothing model, with the results revealing that prediction performance provided by the LME model is more stable than using the other methods. Furthermore, product types in the data are regarded as a random effect in the LME model, hence demands of all types can be predicted simultaneously using a single LME model. However, some approaches require splitting the data into different type categories, and then predicting the type demand by establishing a model for each type. This feature also demonstrates the practicability of the LME model in real business operations.

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Electricity consumption forecasting in Italy using linear regression models

Cited by 503 publications

References 22 publications

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Benchmarking and tracking domestic gas and electricity consumption at the local authority level

Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

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