A hybrid methodology for distribution level photovoltaic power production forecasting verified at the distribution system of Cyprus

Abstract

Accurate photovoltaic (PV) power forecasting is an essential tool that enables the efficient integration of solar electricity and energy trading. This work proposes a novel methodology for the implementation of a forecasting tool that provides aggregated PV production day‐ahead forecasts for the PV systems installed at a distribution level. Specifically, the tool is a cloud‐based platform, comprising of a data quality block, a weather forecasting model, a machine learning power prediction step and an up‐scaling aggregation stage. In this context and in the absence of a fully observable distribution system, the aggregated PV generation was estimated using a clustering approach and up‐scaling the measured generation of reference systems to the aggregated installed capacity. The results demonstrated that the implemented tool exhibited high forecasting accuracies, lower than 10% given by the mean absolute percentage error when applied to the distribution system of Cyprus. Furthermore, the comparative benchmarking of the up‐scaling techniques against the estimated aggregated PV generation demonstrated that the best‐performing approach was the hybrid model, which provided a normalised root mean square error of 10.29% and mean absolute percentage error of 9.11%. Finally, useful information is provided for establishing a robust day‐ahead forecasting methodology that is based on an optimal supervised learning and hybrid up‐scaling approach.

Homeowners’ Perceptions of Renewable Energy and Market Value of Sustainable Buildings

“…Pafos, the fifth-largest city in Cyprus, named the European Capital of Culture in 2017, is a popular tourist destination [105] coastal city located in the southwestern part of Cyprus, a Mediterranean island country that has set ambitious targets for increasing the share of renewable energy in its energy mix [106,107]. Pafos has unique characteristics of interest to researchers in various fields [108][109][110][111][112][113][114][115], such as its coastal location, the growing tourism industry, and typically Mediterranean climate characterized by long, hot, and dry summers and mild winters with abundant sunshine, which makes it an attractive location for real estate development and the use of renewable energy sources. In terms of building standards, the Cypriot government has introduced regulations to promote energy-efficient building design and construction, and the use of renewable energy sources, such as solar panels and geothermal energy, is becoming increasingly common.…”

Direct Short-Term Net Load Forecasting Based on Machine Learning Principles for Solar-Integrated Microgrids

“…In the field of load forecasting, BNNs provided high-precision forecasts, while also estimating automatically the forecasting error. It was also shown that low execution time is required for their training process [18]. Other advantages of BNNs include the simplicity of managing hyperparameters, low sensitivity to small datasets and prevention of overfitting (through an automated procedure for adjusting the regularization term) [18].…”

“…To this end, BNNs use probabilistic layers that capture uncertainty (in the predictive output) over weights and activations [47]. The development of such models involves the integration of stochastic components into the network architecture, that consist of a set of interconnected Bayesian neurons (or nodes) [18]. The nodes comprising the network are organized in three layers, namely the input, hidden, and output layers (as depicted in Fig.…”

“…The model provided a root mean square error (RMSE) of 0.30 kW, 0.36 kW, and 0.42 kW for forecast horizons of 1-, 2-, and 3-hours, respectively. The authors in [18] proposed a hybrid approach for day-ahead PV power generation forecasting at the distribution system of Cyprus. The approach yielded a nRMSE and a MAPE of 10.29% and 9.11%, respectively.…”