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Day-Ahead Hourly Forecasting of Power Generation From JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Senior Member, IEEE, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri, Debora Mucci Abstract—The ability to accurately forecast power generation • Monitoring: mismatches between power forecasts and from renewable sources is nowadays recognised as a fundamental the actually generated power may be also used by energy skill to improve the operation of power systems. Despite the producers to monitor the plant operation, to evaluate the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, natural degradation of the efficiency of the plant due and infer the impact of single components in providing accu- to the aging of some components (see [4]) or for early rate predictions. In this paper we extensively compare simple detection of incipient faults. forecasting methodologies with more sophisticated ones over 32 For the previous reasons, the topic of renewable energy photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather conditions forecasting has been also object of some recent textbooks like and weather forecasts on the prediction of PV power generation. [5] and [6] that provide overviews of the state-of-the-art of the most recent technologies and applications of renewable Index Terms—PV plants; Machine Learning algorithms; power energy forecasting. In this context, the objective of this paper generation forecasts. is to compare different methodologies to predict day-ahead hourly power generation from PV power plants. I. INTRODUCTION A. State of the Art IGH penetration levels of Distributed Energy Resources Power generation from PV plants mostly depends on (DERs), typically based on renewable generation, in- H some meteorological variables like irradiance, temperature, troduce several challenges in power system operation, due to humidity or cloud amount. For this reason, weather forecasts the intrinsic intermittent and uncertain nature of such DERs. are a common input to forecasting methodologies for PV In this context, it is fundamental to develop the ability to generation. Depending on the specific problem at hand, accurately forecast energy production from renewable sources, forecasts may be also necessary at different spatial and like solar photovoltaic (PV), wind power and river hydro, temporal scales, as from high temporal resolutions (i.e., of to obtain short- and mid-term forecasts. Accurate forecasts the order of minutes) and very localized (e.g., off-shore provide a number of significant benefits, namely: wind farms) to coarser temporal resolutions (e.g., hours) • Dispatchability: secure power systems’ daily operation and covering an extended geographical area (e.g., a region mainly relies upon day-ahead dispatches of power plants or a country) for aggregated day-ahead power dispatching [1]. Accordingly, meaningful day-ahead plans can be per- problems. At the same time, very different approaches and formed only if accurate day-ahead predictions of power methodologies have been explored in the literature, based on generation from renewable sources, together with reliable statistical, mathematical, physical, machine learning or hybrid predictions of the day-ahead load consumption forecasts (i.e., a mix of the previous) approaches. For example, [3] (e.g., see [2]) are available; uses fuzzy theory to predict insolation from data regarding • Efficiency: as output power fluctuations from intermittent humidity and cloud amount, and then uses Recurrent sources may cause frequency and voltage fluctuations Neural Networks (RNNs) to forecast PV power generation. in the system (see [3]), some countries have introduced Autoregressive (ARX) methods are used in [7] for short-term penalties for power generators that fail to accurately forecasts (minute-ahead up to two hour-ahead predictions) predict their power generation for the next day; thus, using spatio-temporal solar irradiance forecast models. A some energy producers prefer to underestimate their day- forecasting model for solar irradiance for PV applications is ahead power generation forecasts to avoid to incur in also proposed in [8]. The presence of particulate matter in penalties in the next day. Such induced conservative the atmosphere (denoted as Aerosol Index (AI)) is used in behaviours are clearly not efficient; [9] to support an artificial neural network (ANN) to forecast PV power generation. L. Gigoni and A. Betti are with i-EM S.r.l, Via Lampredi 45, 57121 Livorno, Italy. E. Crisostomi, A. Franco and M. Tucci are with the Department of Energy, As for the specific day-ahead hourly forecasting PV Systems, Territory and Constructions Engineering, University of Pisa, Pisa, power problem, [10] use add a least-square optimization of Italy. Email: [email protected]. F. Bizzarri is with Enel Green Power S.p.A. Numerical Weather Prediction (NWP) to a simple persistence D. Mucci is with Renewable Energy Management, Enel Green Power S.p.A. model, to forecast solar power output for two PV plants JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 2 in the American Southwest. A multilayer perceptron was to invalidate simple comparisons of different forecasting used in [11] to predict the power output of a grid-connected methodologies on the basis of the final accuracy results alone. 20-kW solar power plant in India. A stochastic ANN was adopted in combination with a deterministic Clear Sky Solar One exception to the previous consideration is provided Radiation Model (CSRM) to predict the power output of by the already mentioned Global Energy Forecasting four PV plants in Italy. A weather-based hybrid method was Competition 2014, where different (sophisticated) forecasting used in [13] as well, where a self-organizing map (SOM), algorithms were in fact compared, and ranked, upon the a learning vector quantization (LVQ) network, a Support same, publicly available, data-set. However, note that the Vector Regression (SVR) method and a fuzzy inference competition only lasted less than three months, thus not approach were combined together to predict power generation allowing one to validate the final rank over different seasons, for a single PV plant. In [14] Extreme Learning Machines and only involved three PV plants. From this perspective, (ELMs) are used to predict the power generation of a our work extends the results of the competition by further PV experiment system in Shanghai. Finally, we refer the comparing the same algorithms that ranked at the first places interested readers to the two recent papers [15], [16], and to of the competition over a longer horizon of time, and over a the references therein, for an extensive review of the literature. more variegate set of different PV plants. A Global Energy Forecasting Competition (GEFCom2014) Accordingly, more specifically, the contributions of this has recently allowed different algorithms to be compared, in paper are the following: a competitive way, to solve probabilistic energy forecasting problems, see [17] for a detailed description of the outcome • We extensively compare four state-of-the-art different of the competition. GEFCom2014 consisted of four tracks “black-box” forecasting methodologies upon the same on load, price, wind and solar forecasting. In the last case, set of data (i.e., k-Nearest Neighbours; Neural Networks; similarly to this paper, the objective was to predict solar Support Vector Regression; and Quantile Random For- power generation on a rolling basis for 24 hour ahead, est). Such methods have been tailored to address this for three solar power plants located in a certain region of specific task of interest. Note that k-Nearest Neighbours, Australia (the exact location of the solar power plants had Quantile Random Forests and Support Vector Regression not been disclosed to the participants of the competition) were the building blocks of the algorithms ranked at the [17]. An interesting result of the competition was that all the first places of the Global Energy Forecasting Competition approaches that eventually ranked at the first places of the for solar power forecasting (i.e., they ranked first, second competition were nonparametric, and actually consisted of a and fifth respectively). We also added Neural Networks wise combination of different techniques. in the comparison as it has been frequently used by many other researchers, as from the previous state of the art; • We further compare the predictions results with those B. Contributions obtained with a very simple second-order regressive The performance of the various forecast models are affected method. While the previous more sophisticated method- by many elements of uncertainties, and in the opinion of the ologies usually outperform such a simple method, still authors it is not always clear how single choices (e.g., the the improvement is not as large as one might expect. choice of a specific prediction methodology over another) Accordingly, second-order regressive methods may still or different factors (e.g., meteorological forecasting errors) be regarded accurate enough for most operations; this contribute to the final prediction error (i.e., in terms of simple comparison is usually missing in the literature, predicted vs. actual power generation). In fact, most of the thus not making it simple to evaluate the gain obtained
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