Abstract
Energy efficiency is a key challenge for building modern sustainable societies. World’s energy consumption is expected to grow annually by 1.6 %, increasing pressure for utilities and governments to fulfill demand and raising significant challenges in generation, distribution, and storage of electricity. In this context, accurate predictions and understanding of population dynamics and their relation to electricity demand dynamics is of high relevance.
We introduce a simple machine learning (ML) method for day-ahead predictions of hourly energy consumption, based on population and electricity demand dynamics. We use anonymized mobile phone records (CDRs) and historical energy records from a small European country. CDRs are large-scale data that is collected passively and on a regular basis by mobile phone carriers, including time and location of calls and text messages, as well as phones’ countries of origin. We show that simple support vector machine (SVM) autoregressive models are capable of baseline energy demand predictions with accuracies below 3 % percentage error and active population predictions below 10 % percentage error. Moreover, we show that population dynamics from mobile phone records contain information additional to that of electricity demand records, which can be exploited to improve prediction performance. Finally, we illustrate how the joint analysis of population and electricity dynamics elicits insights into the relation between population and electricity demand segments, allowing for potential demand management interventions and policies beyond reactive supply-side operations.
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- 1.
There is a large amount of tourism in the country. Over the time analyzed, on roughly one in four people connecting to a cell tower were not from the local country.
- 2.
Shown are countries with highest amount of visitors out of more than 50 countries.
- 3.
We train on 150 days and test on subsequent 30 days. We optimize regularization parameters on a sequence of sequential 180 day blocks and assess prediction on a final set of 30 unseen days.
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Wheatman, B., Noriega, A., Pentland, A. (2016). Electricity Demand and Population Dynamics Prediction from Mobile Phone Metadata. In: Xu, K., Reitter, D., Lee, D., Osgood, N. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science(), vol 9708. Springer, Cham. https://doi.org/10.1007/978-3-319-39931-7_19
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DOI: https://doi.org/10.1007/978-3-319-39931-7_19
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