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Sensors 2018, 18(8), 2529; https://doi.org/10.3390/s18082529

Sensorless PV Power Forecasting in Grid-Connected Buildings through Deep Learning

1
B2B Solution R&D Center, CTO, LG Electronics, 51, Gasan digital 1-ro, Geumcheon-gu, Seoul 08592, Korea
2
Department of Computer Science and Engineering, Korea University, Anam-Dong, Sungbuk-gu, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Received: 24 June 2018 / Revised: 28 July 2018 / Accepted: 30 July 2018 / Published: 2 August 2018
(This article belongs to the Section Sensor Networks)
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Abstract

Existing works in photovoltaic (PV) power generation focus on accurately predicting the PV power output on a forecast horizon. As the solar power generation is heavily influenced by meteorological conditions such as solar radiation, the weather forecast is a critical input in the prediction performance. However, the weather forecast is traditionally considered to have coarse granularity, so many are compelled to use on-site meteorological sensors to complement it. However, the approach involving on-site sensors has several issues. First, it incurs the cost in the installation, operation, and management of the sensors. Second, the physical model of the sensor dynamics itself can be a source of forecast errors. Third, it requires an accumulation of sensory data that represent all seasonal variations, which takes time to collect. In this paper, we take an alternative approach to use a relatively large deep neural network (DNN) instead of the on-site sensors to cope with the coarse-grained weather forecast. With historical PV output power data from our grid-connected building with a rooftop PV power generation facility and the publicly available weather forecast history data, we demonstrate that we can train a six-layer feedforward DNN for the day-ahead forecast. It achieves the average mean absolute error (MAE) of 2.9%, comparable to that of the conventional model, but without involing the on-site sensors. View Full-Text
Keywords: solar power; deep learning; PV power output forecast; on-site meteorological sensors; cost reduction; accuracy solar power; deep learning; PV power output forecast; on-site meteorological sensors; cost reduction; accuracy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Son, J.; Park, Y.; Lee, J.; Kim, H. Sensorless PV Power Forecasting in Grid-Connected Buildings through Deep Learning. Sensors 2018, 18, 2529.

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