Day-ahead probabilistic PV generation forecast for buildings energy management systems

El-Baz, Wessam; Tzscheutschler, Peter; Wagner, Ulrich

doi:10.1016/j.solener.2018.06.100

Cited by 76 publications

(27 citation statements)

References 43 publications

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“…Ito et al [50] adopted a geographical area approach for predicting the output from large-scale gridconnected PV, for example, one prediction point per region. A building scale PV model which incorporates partial shading [51] may account for extremely localized effects such as a cloud passing over the building and may be more beneficial in this context.…”

Section: Photovoltaic (Pv) Modelmentioning

confidence: 99%

Development of a Framework for Activation of Aggregator Led Flexibility

O’Connell

Keane

2021

Energies

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This paper presents a novel framework architecture for an online, real-time flexibility assessment and activation platform targeted at unlocking the flexibility potential of commercial buildings and smaller industrial sites, thereby enabling greater levels of renewable grid integration. Renewable integration targets in Europe of up to 40% of power generation from renewable sources by 2030 and over 90% by 2050 aim to decarbonize the electrical grid and increase electrification of transport, industry, and buildings. As renewable integration targets increase, participation in flexibility programs will be required from a much greater range of buildings and sites to balance grids hosting high levels of renewable generation. In this paper, an online implementation of a standardized flexibility assessment methodology, previously developed for offline contract negotiations between stakeholders, is modified to automate the assessment. The automated assessment is then linked to an aggregator-based multi-building or site optimization stage, enabling increased participation of multiple buildings and sites. To implement the assessment, models for individual flexible systems were reviewed, selected, and adapted, including physics-based, data-driven, and grey-box models. A review of optimization for flexibility found mixed-integer linear programming to be the optimal approach for the selection of flexible systems for demand response events.

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Section: Photovoltaic (Pv) Modelmentioning

confidence: 99%

Development of a Framework for Activation of Aggregator Led Flexibility

O’Connell

Keane

2021

Energies

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“…The second option to obtain the distribution of the random variable is called nonparametric approach, in which quantile function is calculated. In existing literature, several methods have been proposed to deal with probabilistic PV energy generation forecast 19,20 . Moreover, the application of probabilistic forecast of PV generation in scheduling the battery and managing flexibility of the system was studied in the literature 21 .…”

Section: Introductionmentioning

confidence: 99%

Data‐driven forecasting of local PV generation for stochastic PV ‐battery system management

Kaffash

Bruninx

2021

Int J Energy Res

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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.

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“…Hong and Fan [ 39 ] provided a review of the state-of-the-art in probabilistic electric load forecasting where they stated that it can be implemented in practically the same cases in which single-valued load forecasts are applied. For example, it has been used for electricity consumption prediction in buildings [ 41 , 42 , 43 ], but also for distributed renewable energy production forecasts, such as photovoltaic power generation or wind speed forecasts [ 44 , 45 ]; applications related to electric vehicles [ 46 ]; and the quantification of the power reserve of a microgrid [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Forecasting for Building Energy Models

Segarra

Ruiz

Bandera

2020

Sensors

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In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data.

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Day-ahead probabilistic PV generation forecast for buildings energy management systems

Cited by 76 publications

References 43 publications

Development of a Framework for Activation of Aggregator Led Flexibility

Development of a Framework for Activation of Aggregator Led Flexibility

Data‐driven forecasting of local PV generation for stochastic PV ‐battery system management

Probabilistic Load Forecasting for Building Energy Models

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