Data‐driven forecasting of localPVgeneration for stochasticPV‐battery system management

Abstract

Summary Power systems face more uncertainty by increasing photovoltaic system installations on the roof of buildings. To optimally manage energy and available flexibility in a building, stochastic optimization is used to take an optimal decision under uncertainty and minimize the operational cost. In stochastic optimization, a scenario set is used as an input to represent the uncertainty in a random variable, PV energy generation in this case. In this paper, a data‐driven method is proposed to obtain the distribution of the random variable and later generate scenario sets representing the uncertainty on day‐ahead PV energy generation installed on the roof of a building. This method is only based on historical PV generation and it does not require any other external data such as weather forecasts. A machine learning‐based technique is applied to forecast the PV energy production following by generating a scenario set for day‐ahead decision‐making. Later, the day‐ahead PV‐battery system management problem is formulated as a two‐stage stochastic optimization while the generated scenario set is the input of this optimization. The proposed algorithm is tested in the day‐ahead scheduling of a PV‐battery system for a commercial building, informed by real‐life measurement data. The results show that the proposed algorithm is able to capture the uncertainty in PV system while providing a cost‐optimal and reliable solution to the application problem. Without using any weather data, the error of the proposed PV energy forecast method reaches to NRMSE = 11.89, while there is 5% reduction in the operational cost of the proposed two‐stage stochastic optimization. Moreover, the proposed algorithm is easy to implement in the energy management system of a building to manage PV‐battery system.

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

“…The approach proposed in [18] was first used in [17] for SPS generation, taking into consideration spatiotemporal dependencies. The approach proposed in [18] has also been used in [54,55]. Several improvements of [18] have also been proposed.…”

Development of a Digital Twin Framework for a PV System to Resolve Partial Shading

“…Design of Data-Driven Twin. Pyranometer fed the direct irradiation, diffused irradiation, and temperature data to the data logger [34] where data is logged concerning GPS timing. The simulation is designed to be executed using the temperature and irradiation data from the real world.…”

A Data-Driven Short-Term PV Generation and Load Forecasting Approach for Microgrid Applications

“…However, DD approaches that focus more on the data might aid in improving the prediction accuracy. To fix this model-centric trend, in recent time, DD approaches have been considered for forecasting PV generation [9] - [10] and load demand [3], [8], [11] - [12]. Authors in [13] have focused on improving forecasts by combining different forecasting models.…”

“…It improves the accuracy of prediction by implementing SVR model. the relevant lagged features, a least absolute shrinkage and selection operator based algorithm is discussed in [10], which derives the important lagged values of historical PV time series data and use those features to predict the day-ahead PV generation using a feed-forward NN (FFNN) method. Similarly, in [17], the authors derive the important lagged values by RReliefF (RRF) algorithm and utilise them to predict 15 min PV generation through a single hidden multi-layer perceptron (MLP) model.…”