Solar Energy Prediction

In efforts to transition away from the use of fossil fuels and their associated environmental problems, a number of strategies have been deployed worldwide in recent decades. Use of renewable energy sources is one such strategy, an effort in which systems that use solar power to generate electricity have come to play an important role [1]. A popular way to harvest this solar power is through the deployment of photovoltaic (PV) systems. The solar panels that make up the key part of these systems are designed to generate a voltage when in the presence of sunlight, effectively converting it into electricity. The amount of electricity generated at any one point of time is dependent on the intensity of the sunlight that reaches the panel surface. This inherent nature of the technology means that production patterns are, in turn, highly dependent on a number of external factors. Principally, the daily, seasonal and yearly cycles, together with a panel’s geographical location and angular orientation in relation to the sun, determine the intensity of the sunlight and therefore the electricity generated [2]. Apart from these time bound factors and methods of installation, the main driver for determining production are the local weather conditions that dictate the status of the atmosphere, representing the hurdles that sunlight needs to travel through before coming into contact with a panel [5].

Weather conditions being variable and complex, the resulting stochastic nature of PV solar energy production differs from many other sources. The burning of gas or coal, for example, are well- understood and predictable processes with which energy can be produced on demand. This difference introduces new challenges in maintaining the delicate balance of electrical grids as solar energy becomes a larger part of the total energy production [3]. One such place recently experiencing growth in its solar energy contribution is the European Union member state of the Netherlands, currently one of the top three PV solar installation markets in Europe and expected to remain so in coming years for the period of 2021 to 2024 [4]. An important part of the growing number of solar panels in the Netherlands are residential roof-mounted PV systems, meant to generate electricity for use in the same household before feeding an eventual surplus back into the overall grid. Regulations dictate that power companies are required to deduct the electricity fed into the system from electricity consumed. These regulations have been in place to stimulate the growth of solar energy but are slated to change. The legal requirement for providers to credit consumers stands to be phased out starting in 2023 and ending in 2031. Expected to replace the current policies are ones resembling those already in place in neighbouring countries like Germany, where market regulators define hourly energy prices one day in advance. These prices fluctuate with expected supply and demand, sometimes dropping into negative numbers. This creates financial incentive for consumers to not contribute to peak supply electrical loads, which now threaten to overload the grid at times [3]. Therefore, it is beneficial to the decision-making processes of participants in day-ahead energy markets to have access to high-quality localized forecasts on the amount of electricity that will be generated, both in the interest of integrating the growing share of solar energy and to meet looming policy changes.

The goal is basically to make a prediction model that makes 24 hourly photovoltaic energy output predictions. This is achieved by using a SARIMAX model with weather data as exogenous variables. Testing for (inter)correlation resulted in solar net thermal radiation as an exogenous variable. The results from the SARIMAX model are then compared to a baseline SARIMA model. Concluding from the results, daytime error is 8.81% lessened when adding solar net thermal radiation. These results however, are limited by the data received. With fewer than two seasonal cycles, the time series cannot be easily converted into a stationary series. This leads to the prediction results not being as much informed by weather variables, but more by its time dependency. The methodology proposes a novel two-step method for selecting high value exogenous variables, based on correlation and intercorrelation. It furthermore puts an emphasis on 24 hour-ahead prediction by using rolling updates on a daily basis, while also predicting photovoltaic energy on a more local and consumer-oriented scale. This makes the methodology of the article a good setup for future work.

[1] Factory zero. Ac- cessed: 2022-02-03.

[2] Mohamed Abdel-Basset, Hossam Hawash, Ripon K. Chakrabortty, and Michael Ryan. PV-Net: An innovative deep learning ap- proach for efficient forecasting of short-term photovoltaic energy production. Journal of

Cleaner Production, 303:127037, June 2021.

[3] Agust ́ın Agu ̈era-P ́erez, Jos ́e Carlos Palomares-Salas, Juan Jos ́e Gonz ́alez
de la Rosa, and Olivia Florencias-Oliveros. Weather forecasts for microgrid energy management: Review, discussion and recom- mendations. Applied Energy, 228:265–278, October 2018.

[4] A.R. Al-Ali. Internet of Things Role in the Renewable Energy Resources. Energy Pro- cedia, 100:34–38, November 2016.

[5] Mike H. Bergin, Chinmay Ghoroi, Deepa Dixit, James J. Schauer, and Drew T. Shin- dell. Large Reductions in Solar Energy Pro- duction Due to Dust and Particulate Air Pollution. Environmental Science & Tech- nology Letters, 4(8):339–344, August 2017.

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Dr. Cor Beyers
Cor Beyers has a PhD from the Eindhoven University of Technology where used chemometrical methods in spectroscopy to predict the concentrations of chemical compounds. He also has an business degree with experience working in industry for multi-nationals such as BASF, PPG and Sherwin Williams. He joined the research group data science to work on the extraction of insights from Big Data through the use of advanced analytical methods to improve business decision making.