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This paper presents a stochastic programming framework for solving the scheduling problem faced by an industrial customer with cogeneration facilities, conventional power production system, and heat only units. The power and heat demands of the customer are supplied considering demand response (DR) programs. In the proposed DR program, the responsive load can vary in different time intervals. In the paper, the heat-power dual dependency characteristic in different types of CHP units is taken into account. In addition, a heat buffer tank, with the ability of heat storage, has been incorporated in the proposed framework. The impact of the market and load uncertainties on the scheduling problem is characterized through a stochastic programming formulation. Autoregressive integrated moving average (ARIMA) technique is used to generate the electricity price and the customer demand scenarios. The daily and weekly seasonalities of demand and market prices are taken into account in the scenario generation procedure. The conditional value-at-risk (CVaR) methodology is implemented in order to limit the risk of expected profit due to market price and load forecast volatilities. [ABSTRACT FROM AUTHOR]

• A novel multivariate probabilistic forecasting method. • The impacts of probabilistic forecasting's performance on hydro-wind dispatch. • Appropriate trade-off between running time and benefit. • Case study that optimizes the long-term optimal dispatch scheme. There are two difficulties in long-term optimal dispatch of hydro-wind hybrid systems. First, monthly runoffs and wind speeds have the dynamic characteristics such as variability, instability, seasonality, heteroscedasticity, linear and nonlinear dynamic correlations. Second, hydro-wind hybrid systems have highly non-convex nonlinear constraints. To overcome the problem, this research develops a novel X-12 seasonal adjustment, vector autoregressive integrated moving average (VARIMA), component generalized autoregressive conditional heteroscedasticity (C-GARCH) and dynamic copula mixed model to estimate the joint probability distribution of runoffs and wind speeds. And then, this paper builds a multistage stochastic mixed-integer linear programming (MILP) with the help of several linearization methods. Finally, the paper compares several probabilistic forecasting models' performances and analyzes their impacts on the dispatch of the hydro-wind hybrid system under different hydrological years. A hydro-wind hybrid system in southwest China is taken as an example. The case study leads to the following conclusions: 1) the more sufficient to capture the dynamic characteristics of variables, the higher benefit will be; 2) it is necessary to increase the scale of scenario tree to reduce the electricity shortfall during the dry year; 3) serious spilled water can be caused by insufficient interregional transmission capacity under the wet year and it is the most appropriate to expand the capacity to 8000 MW; 4) the model proposed in this paper can increase the economic benefit by 0.466 × 10 9 C N Y , 1.775 × 10 9 C N Y and 0.400 × 10 9 C N Y during the normal, dry and wet year, respectively. [ABSTRACT FROM AUTHOR]

Electricity prices have rather complex features such as high volatility, high frequency, nonlinearity, mean reversion and non-stationarity that make forecasting very difficult. However, accurate electricity price forecasting is essential to market traders, retailers, and generation companies. To improve prediction accuracy using each model’s unique features, this paper proposes a hybrid approach that combines the wavelet transform, the kernel extreme learning machine (KELM) based on self-adapting particle swarm optimization and an auto regressive moving average (ARMA). Self-adaptive particle swarm optimization (SAPSO) is adopted to search for the optimal kernel parameters of the KELM. After testing the wavelet decomposition components, stationary series as new input sets are predicted by the ARMA model and non-stationary series are predicted by the SAPSO-KELM model. The performance of the proposed method is evaluated by using electricity price data from the Pennsylvania-New Jersey-Maryland (PJM), Australian and Spanish markets. The experimental results show that the developed method has more accurate prediction, better generality and practicability than individual methods and other hybrid methods. [ABSTRACT FROM AUTHOR]

Wind speed forecasting plays a vital role in dispatch planning and operational security for wind farms, however, its difficulty is commonly accepted. This paper develops a nonlinear autoregressive (exogenous) model for one-day-ahead mean hourly wind speed forecasting, where general regression neural network is employed to model nonlinearities of the system. Specifically, this model is a two-stage method consisting of the model selection and training stage along with the iterative forecasting and correcting stage. In the former stage, the model is in the series-parallel configuration, and its test error is estimated by the cross-validation (CV) method. With the help of ARIMA identification results, CV errors are minimized by the Fibonacci search method to select the best lag structure and the only adjustable parameter. In the latter stage, the model is in the parallel configuration, and the so-called leave-one-day-out resampling method is proposed to iteratively estimate correction parameters for horizons up to 24 h ahead, which holds out each full-day data segment from the sample of observations in turn to faithfully reproduce the entire process of training, iterative forecasting and correcting in the in-sample period. Finally, the out-of-sample corrected forecasts can be successively obtained by using the model selected and trained in the former stage and the correction parameters estimated in the latter stage. Furthermore, effectiveness of this model is verified with four real-world case studies of two wind farms in China. [ABSTRACT FROM AUTHOR]

Smart grid is a platform that enables the participants of electricity market to adjust their bidding strategies based on Demand-Side Management (DSM) models. Responsiveness of the market participants can improve reliability of system operation as well as capital cost investments. In this regard, the accurate forecast of electricity price and demand in smart grids is an important challenge as their strong correlation makes a separate forecasting to be ineffective. Therefore, this paper proposes a novel hybrid algorithm for simultaneous forecast of price and demand that uses a set of effective tools in preprocessing part, forecast engine and tuned algorithm. To highlight our contributions, the proposed forecast algorithm classified into three main parts. The first part employs a new Flexible Wavelet Packet Transform (FWPT) to decompose a signal into multiple terms at different frequencies, and a new feature selection method that employs Conditional Mutual Information (CMI) and adjacent features in order to select valuable input data. The second part consists of a novel Multi-Input Multi-Output (MIMO) model based on Nonlinear Least Square Support Vector Machine (NLSSVM) and Autoregressive Integrated Moving Average (ARIMA) in order to model the linear and nonlinear correlation between price and load in two stages. The final part employs a modified version of Artificial Bee Colony (ABC) algorithm based on time-varying coefficients and stumble generation operator, called TV-SABC, in order to optimize NLSSVM parameters in a learning process. The proposed hybrid forecasting algorithm is evaluated on several real and well-known markets illustrating its high accuracy in simultaneous forecast of electricity price and demand. Moreover, the interactive effects of demand-side management programs on load factor (load curve) and price signal are investigated by numerical indices. [ABSTRACT FROM AUTHOR]

District heating is expected to play an essential role in the cost-effective decarbonisation strategy of many countries. Resource-optimised management of district heating networks depends on a wide range of factors, including demand forecasting, operational flexibility, and increasingly volatile market conditions. However, traditional operations often still rely on static models and rather simple heuristics, while holistic optimisation requires dynamic cross-domain interoperability to allow the consideration of all these factors. This paper demonstrates a proof-of-concept for a knowledge graph based optimisation problem to minimise total heat generation cost for a district heating provider. The optimisation follows a hierarchical approach based on a merit-order principle and is embedded in a model predictive control framework to allow the system to incorporate most recent information and react to disturbances promptly. A detailed sensitivity study is conducted to identify key model parameters and assess the impact of anticipated changes in regulation and market conditions. Simulation-based optimisation is used to determine the short-term heat generation mix based on data-driven gas consumption models and day-ahead forecasts for the network's energy demand and grid temperatures. Seasonal autoregressive integrated moving average models with exogenous predictor variables are found to be sufficiently accurate and precise. The effectiveness of the approach is demonstrated for a case study of an existing heating network of a midsize town in Germany, where a reduction of approximately 20% and 40% compared to baseline operational data is obtained for operating cost and CO 2 emissions, respectively. [Display omitted] • Developed heat generation optimisation framework for district heating providers. • Identified key cost drivers and market dynamics using a detailed sensitivity analysis. • Developed high accuracy time series forecasting models for key input variables. • Assessed optimisation effectivity using a German municipal utility case study. • Improvement potential for operational cost and CO 2 emissions both exceed 20%. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. [ABSTRACT FROM AUTHOR]

Global Horizontal Irradiation forecasts are necessary for an efficient use of fluctuating energy output from photovoltaic plants. The purpose of this paper is to provide efficient, easy-to-implement Global Horizontal Irradiation forecasts on an hourly basis for a medium-term horizon (longer than 48 h). These forecasts are essential for the strategic deployment of tasks including unit commitment, transmission management, trading, hedging, planning, asset optimization, maintenance scheduling and spinning of power units. Nonetheless, forecasting models for medium term horizons are scarce. This work intends to bridge that gap by proposing a method based on an adjusted combination of moving averages which includes the yearly cycle in addition to the day–night cycle. This method is straightforward to implement and easy to integrate into corporate computing systems. We compare our approach with several well-known alternative methods, such as deep recurrent neural networks or autoregressive integrated moving average models, among others. The results show that recurrent neural networks are 10% more accurate than the second best method, and 90% better than our proposal for very short horizons (up to 5 h ahead). However, this advantage is lost when longer horizons are tested, especially for horizons longer than 30 h ahead, for which our method produces forecasts which are 20% more accurate than those made by recurrent neural networks. The reason behind that improvement at longer horizons is the inclusion of the yearly cycle in the proposed approach. • There is a rich literature on short-term photovoltaic energy forecasting methods. • Many tasks in photovoltaic plants need forecasting methods for medium-term horizons. • A new method for medium-term photovoltaic energy forecasting is developed and tested. • Many methods produce accurate forecasts for short-term horizons, deep learning best. • The proposal outperforms many forecasting methods for medium-term horizons. [ABSTRACT FROM AUTHOR]

• A new meta-learning recommendation system is proposed. • The new system concerned two-stage (subjective and objective) user preferences. • Multi-objective decision making algorithms (MODMA) are used in option optimization. • A new "walking slide method" aimed at some extremely special cases is proposed. • The new system is validated on real buildings, and its generalizability is proved. Building data forecasting plays an increasingly important role in building energy savings. However, the one-fits-all model cannot satisfy all the requirements of multiple application scenarios and user preferences. Motivated by the need to bridge the research gap between different user preferences (application scenarios) and energy prediction model recommendation systems, this paper proposes a novel meta-learning strategy based on an artificial neural network recommendation system. This strategy is employed for real-time cooling loads, coefficients of performance prediction and optimal prediction model recommendations. The data set is composed of 40 cases from five factory buildings. After the predictions and recommendations are obtained for all cases, the two-stage user preferences are considered based on multi-objective decision-making algorithms. Then, a new model termed the "walking slide method", is proposed to predict some special cases. This study shows that the seasonal autoregressive integrated moving average model and random forest model achieve the best prediction accuracy and the minimum computation cost separately for most cases, while the long short-term memory is the best model when considering the two criteria. The variances between the different cases lead to a lower cross-validation score (approximately 65%), but a higher success rate (over 99%) for the recommendation performance. In addition, in the more complex application scenarios, a lower prediction accuracy and recommendation success rate will be obtained. In most cases, the use of a prediction combined with a monitoring system is the best choice. Last, the reliability of the results is verified by application studies. This work provides a scientific basis for energy prediction applications based on user preferences. [ABSTRACT FROM AUTHOR]

• Advanced computational model aims at developing efficient solution techniques. • Complex features of electricity price can be well captured by the proposed model. • The proposed model can adjust its parameters adaptively according to the changing of input sample. • Validation and verification of numerical models through the real electricity markets. With the large-scale renewable energy integration into the power grid, the features of electricity price has become more complex, which makes the existing models hard to obtain a satisfactory results. Hence, more accurate and stable forecasting models need to be developed. In this paper, a new adaptive hybrid model based on variational mode decomposition (VMD), self-adaptive particle swarm optimization (SAPSO), seasonal autoregressive integrated moving average (SARIMA) and deep belief network (DBN) is proposed for short term electricity price forecasting. The effectiveness of the proposed model is verified by using data from Australian, Pennsylvania-New Jersey-Maryland (PJM) and Spanish electricity markets. Empirical results show that the proposed model can significantly improve the forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]