As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

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

Short-term load forecasting is an essential instrument in power system planning, operation, and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the other hand, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations, and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.

/pdf/short-term-load-forecasting-based-on-a-semi-parametric-4pkbxprv30.pdf

Short-Term Load Forecasting Based on a Semi-Parametric Additive Model

The modernisation of the world has significantly reduced the prime sources of energy such as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have been a major focus nowadays to meet the world's energy demand and at the same time to reduce global warming. Among these energy sources, solar energy is a major source of alternative energy that is used to generate electricity through photovoltaic (PV) system. However, the performance of the power generated is highly sensitive on climate and seasonal factors. The unpredictable behaviour of the climate affects the power output and causes an unfavourable impact on the stability, reliability and operation of the grid. Thus an accurate forecasting of PV output is a crucial requirement to ensure the stability and reliability of the grid. This study provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods. Advantages and disadvantages of each method are summarised, based on historical data along with forecasting horizons and input parameters. Finally, a comprehensive comparison between machine learning and metaheuristic methods is compiled to assist researchers in choosing the best forecasting technique for future research.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2018.5649

Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

Day-ahead electricity price forecasting via the application of artificial neural network based models

With the appearance of electricity markets, the variation of the price of electricity will influence usage custom of electric energy. This will complicate short-term load forecasting and challenge the existing forecasting methods that are applied to a fixed-price environment. In regard to the influence of real-time electricity prices on short-term load, a model to forecast short-term load is established by combining the radial basis function (RBF) neural network with the adaptive neural fuzzy inference system (ANFIS). The model first makes use of the nonlinear approaching capacity of the RBF network to forecast the load on the prediction day with no account of the factor of electricity price, and then, based on the recent changes of the real-time price, it uses the ANFIS system to adjust the results of load forecasting obtained by RBF network. This system integration will improve forecasting accuracy and overcome the defects of the RBF network. As shown in this paper by the results of an example of factual forecasting, the model presented can work effectively.

RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment

According to the idea of phase space reconstruction of scalar time series, phase space reconstruction of multivariate time series based on the short-term electric load is presented. The good time delay is chosen for each scalar time series by mutual information. The method to get the minimum embedding dimension is based on minimum forecasting errors. The choice of optimal neighbor points is presented in this paper according to Euclidean distance and degree of correlation between forecast point and its nearest neighboring points. Then one-rank local linear model based on multivariate time series is proposed. The daily load forecasting for a certain district in Chongqing is respectively carried out by the method presented in this paper. Through the analysis of the obtained forecasting results it is shown that the method presented in this paper is more accurate than that of scalar time series.

http://ieeexplore.ieee.org/iel5/4488177/4524335/04524543.pdf

The research of local linear model of short term electrical load on multivariate time series

The geographic information systems (GIS) platform is used in spatial load forecasting (SLF) and the mathematical models of the substation location and capacity optimisation problems are established in this paper. Based on the theory of network flow, a new multi-period optimal selecting algorithm of substation is proposed, which could determine the reasonable location, capacity and time that are put into the running of the substation. The real instance shows that this algorithm could solve the multi period optimal selecting problem of substations in distribution system planning (DSP) successfully, and has a practical value.

GIS based distribution system spatial load forecasting and the optimal planning of substation location and capacity

Oil-paper insulation plays an important role in the internal insulation of power transformers. Aging of transformer insulation is a gradual procedure which can eventually threaten the safe operation of power transformers. During long-term operation of power transformers, oil-paper insulation is exposed to multiple stresses, including electrical, thermal, mechanical factors and so on. Lot of vigor is given to maintenance of the health of transformers in transmission system. Usually the reliability of power transformer is very high and is expected to have a long service life. But according to the recent report and observations, many transformers and reactors have failed prematurely due to the resultant formation of copper sulfide in the windings, which has attracted wide attention. In the past decade, considerable amount of work has been carried out to understand the long term effects of corrosive sulfur in mineral insulating oils, which has been recognized as the culprit of failure of some shunt reactors and power transformers. However, the problem of copper corrosion is very complex because of the complicated existence of sulfur element in transformers. Depending on oil, there are many different sulfur compounds present in oil. Other components of transformer, like pressboard, paper, glue, gasket, contaminated oil deliver hose etc are all potential sources of sulfur of one form or the other, either independently or due their interaction with oil. It is not one single corrosive sulfur compound that is responsible for all corrosive sulfur issues. Therefore the main sources, detection method, corrosive mechanism, damage and suppression measures are still the puzzled research focus. This paper present a comparatively detailed summary of research status about this phenomenon, and some useful research proposals and findings are introduced as well.

Summary of research on corrosive sulfur in oil-paper insulation system

A novel spatial load forecasting (SLF) methodology for distribution network is proposed, cellular automata (CA) theory is used to simulate the process of urban land-use dynamic development, and forecast future land-use types of small-areas. According to fact of urban development, ascertain iterative time and adjustive time of transition rules of CA. In order to eliminate redundancies, overcome faults that land-use rules is always static and suffer influence from subjectivity in traditional methods, rough sets (RS) theory is used to carry out attribute-reduction for potential influencing factors during adjustive time step by step, and obtain dynamic transition rules of CA. Finally, the validity of the proposed method is validated by an actual example.

Zhou Quan

Papers

RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment

The research of local linear model of short term electrical load on multivariate time series

GIS based distribution system spatial load forecasting and the optimal planning of substation location and capacity

Summary of research on corrosive sulfur in oil-paper insulation system

Rough Sets and Cellular Automate Applied to Spatial Load Forecasting