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The accurate prediction of power load is helpful to make reasonable power generation plans and scientific dispatching schemes and achieve the goal of energy saving and emission reduction. There are many factors influencing the power load, and there may be a nonlinear relationship between the power load and these influencing factors. A new fractional multivariate grey Bernoulli model, referred to as MFGBM (q, r, N), is developed in this article for the short-term prediction of power load. The fractional differential equation and fractional accumulation generation are integrated into MFGBM (q, r, N). Second, this paper improves the grey wolf algorithm to better optimize many parameters in the model. The algorithm is improved by using a chaotic Tent map to optimize the initial population composition, adding inertia weights to change the position vector of the grey wolf, and introducing a nonlinear function and Lévy flight to enhance local exploitation and global exploration ability. Finally, this paper selects the daytime and nighttime power loads in Australia and takes the electricity price, humidity, and temperature as the influencing factors to validate the prediction capability of MFGBM (q, r, N). Our findings indicate that MFGBM (q, r, N) is highly applicable to short-term power system prediction. • The multivariate grey Bernoulli model is proposed to predict short-term power load. • The improved grey wolf algorithm is proposed to avoid falling into local optimum. • 3. The non-singular Caputo fractional derivative and Laplace transform are introduced. • The fractional-order operator is introduced to reduce the influence of randomness. [ABSTRACT FROM AUTHOR]

Fifth-generation district heating and cooling (5GDHC) systems have the potential to provide simultaneous heating and cooling, allowing for energy exchange between users with different needs. However, their viability in mild climates with a higher share of cooling demand remains unclear. In this paper, we propose a framework for assessing the engineering, economic and environmental performance of a 5GDHC system compared to a state-of-the-art business-as-usual solution and demonstrate it through a practical case study for a university campus in Melbourne, Australia. When accessible heat sources and sinks are available, the 5GDHC system provides a cost-effective solution, with annual cost savings between 9 and 29 % and GHG emissions reduction between 25 and 58 % compared to an already advanced business-as-usual system. Additionally, by using peak off-peak tariffs and an hourly emission factor for the electricity consumed, we demonstrate the 5GDHC operational flexibility in pursuing different objectives, such as minimising cost or emissions, respectively. The results suggest that 5GDHC systems are an economically and environmentally viable solution in milder climates, and a successful implementation of 5GDHC in Australia can create new market opportunities and pave the way for its adoption in other countries with similar climatic conditions and no established history of district heating systems. • 5GDHC systems are analysed in a new context with a milder climate. • A multi-dimensional assessment framework is proposed. • The framework is demonstrated through a practical case study in Melbourne, Australia. • Flexibility is assessed through varying electricity tariffs and emission factors. • The strong performance of the 5GDHC system offers insights into similar contexts. [ABSTRACT FROM AUTHOR]

To a large extent, electricity price prediction is a daunting task because it depends on factors, such as weather, fuel, load and bidding strategies etc. Those features generate a lot of fluctuations to electricity price. As a type of RNN, LSTM has a good performance on processing time series data as well as some nonlinear and complex problems. To explore more accurate electricity price forecasting approach, in this paper, a new hybrid model based on wavelet transform and Adam optimized LSTM neural network, denoted as WT-Adam-LSTM, is proposed. After the wavelet transform, nonlinear sequence of electricity price can be decomposed and processed data will have a more stable variance, and the combination of Adam, one of efficient stochastic gradient-based optimizers, and LSTM can capture appropriate behaviors precisely for electricity price. This study presented four cases to verify the performance of the hybrid model, and the dataset from New South Wales of Australia and French were adopted to illustrate the excellence of the hybrid model. The results show that the proposed model can significantly improve the prediction accuracy. • Long short-term Memory (LSTM) is applied for electricity price forecasting. • Adam is used as optimizer for LSTM. • Pauta criterion and Min-max normalization are used as preprocessing methods for price data. • Wavelet transform is used to decompose the electricity price series into a set of better-performing constitutive series. • Four cases proved that proposed WT-Adam-LSTM outperforms the existing models reported in the literature. [ABSTRACT FROM AUTHOR]