Review of photovoltaic power forecasting

To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.

Forecasting of photovoltaic power generation and model optimization: A review

Solar photovoltaic generation forecasting methods: A review

Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review

Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for the system reliability and promoting large scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machine. In the process, the weather conditions are firstly divided into four types which are clear sky, cloudy day, foggy and rainy day. One-day-ahead PV power output forecasting model for single station is derived based on the weather forecasting data and historically actual power output data as well as the principle of Support Vector Machine (SVM). After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected photovoltaic systems is effective and promising.

Forecasting power output of photovoltaic system based on weather classification and support vector machine

The rapidly developing data center industry results in a large amount of energy consumption. Considering its unique demand characteristics, it becomes desirable to improve its energy efficiency by reusing its waste heat. In this paper, an integrated energy management scheme is proposed to optimize the operation cost for a data center microgrid. The waste heat recovered from data center operation is optimally scheduled with other resources in the integrated energy management model to minimize the operation cost of data center microgrid. Taking the uncertain nature of renewable supply, power and heat demand into consideration, a two-stage stochastic programming model is formulated and analyzed under different test cases. The numerical study results demonstrate the effectiveness of the proposed integrated energy management model.

Integrated Stochastic Energy Management for Data Center Microgrid Considering Waste Heat Recovery

Internet data centers are booming in the last few years, leading to gigantic amount of energy consumption and greenhouse gas emission. In this paper, a data center microgrid including conventional units, energy storage, and renewable generation is modeled to optimize its electricity bill and carbon footprint. Considering the randomness of electricity price, renewable output and workload distribution, the volatility risk is incorporated to measure the associated operation risk. Then, a day-ahead emission-aware stochastic resource planning scheme is formulated to decide the strategy on power procurement, energy storage operation, batch workload allocation, and unit commitment of conventional units. Simulation results show the effectiveness of the proposed approach.

Emission-aware stochastic resource planning scheme for data center microgrid considering batch workload scheduling and risk management

Capacity and output power forecasting have great significance in seamless integration of renewable energy to the grid. However, the uncertainty of wind power and intermittence of wind energy are the main factors which affect forecasting precision. The wind power output data can be treated as a signal stream which has characteristics for possible wind capacity forecasting. Hilbert-Huang Transforms (HHT) and Hilbert spectral analysis have been applied extensively to analysis nonlinear and non-stationary stochastic signal. The time series of wind power output has been transformed into certain signals with different frequencies. Each signal is taken as input data joining with wind speed data to establish Artificial Neural Network (ANN) forecasting model. The models are combined together to obtain the final results on potential wind power output. This paper proposes HHT-ANN model for wind power forecasting. A case study of a wind farm in Texas, U.S shows that the MRE of proposed method is lower than the traditional ANN approach.

Peng Wang

Papers

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Forecasting power output of photovoltaic system based on weather classification and support vector machine

Integrated Stochastic Energy Management for Data Center Microgrid Considering Waste Heat Recovery

Emission-aware stochastic resource planning scheme for data center microgrid considering batch workload scheduling and risk management

Short term wind power forecasting using Hilbert-Huang Transform and artificial neural network