Photovoltaic power generation forecasting is an important topic in field of sustainable power system design, energy conversion management and smart grid construction. Difficulties arise while the generated PV power is usually unstable due to the variability of solar irradiance, temperature and other meteorological factors. In this study, a hybrid ensemble deep learning framework is proposed to forecast short-term photovoltaic power generation in a time series manner. Two LSTM neural networks are employed working on temperature and power outputs forecasting respectively. The forecasting results are flattened and combined with a fully connected layer to enhance the forecasting accuracy. Moreover, we adopted the attention mechanism for the two LSTM neural networks to adaptively focus on input features that are more significant in forecasting. Comprehensive experiments are conducted with recently collected real-world photovoltaic power generation datasets. Three error metrics were adopted to compare the forecasting results produced by attention LSTM model with state-of-art methods, including the persistent model, the auto-regressive integrated moving average model with exogenous variable (ARIMAX), multi-layer perceptron (MLP) and the traditional LSTM model in all four seasons and various forecasting horizons to show the effectiveness and robustness of the proposed method.

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

Solar forecasting is one of the most promising approaches to address the intermit PV power generation by providing predictions before upcoming ramp events. In this paper, a novel multi-step forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time-series models and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal-spatial (ST) information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach.

Dynamic performance of maximum power point tracking (MPPT) algorithms is important to ensure high power yield under practical operating conditions. In this paper, after reviewing three dynamic test procedures, including stepped oper- ation procedure, day-by-day operation procedure, and EN50530 dynamic test procedure, three typical MPPT algorithms such as the fixed-step-size perturb and observe (P&O), variable-step- size incremental conductance (VSSINC), and hybrid-step-size Beta method are evaluated experimentally under the EN 50530 dynamic test procedure. Two dynamic EN50530 test sequences are adopted for the performance evaluation to cover different irradiance changing conditions. The PV model for EN50530 dynamic test sequences is built and the effects of wrong-step changes by using three MPPT algorithms are analyzed systemat- ically. The experimental comparison of three MPPT algorithms in terms of the tracking routines, accumulated energy, and tracking efficiency is presented. The research shows that the 0.5% fixed- step-size P&O may fail to track the MPP due to the tracking drift while the beta algorithm exhibits the highest tracking efficiency under both dynamic sequences. The average tracking efficiency improvement of the beta algorithm compared with other two algorithms are experimentally measured as 24.2% and 18.8% respectively.